Datasets:
DannyJun
/
real_spr_basket_v1

Dataset card Data Studio Files
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1
image
imagewidth (px)
320
320
wrist
imagewidth (px)
320
320
conversations
dict
annotation
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0 values
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_69><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[119, 17], [148, 35], [167, 69], [166, 92], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ë±ĭ, ìľ¾, Ôij, à¬ī, ðĸ¥, íĵ¶, ðŁİij], [áĪ¨, ìĦIJ, ḻ, ãĪª, âĶ½, ðĿĻ¸, ðŁİij], [ìĿ¿, ðŁķŀ, áĴĮ, âļĤ, âĨ·, âĿ«, ðŁİij], [Ѩ, ðŁĸ¶, áĭ©, á¶ı, â½Ĺ, âºŁ, ðŁİij]]." ] }
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{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_75><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_69><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[120, 17], [158, 41], [167, 74], [166, 97], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[áĪ¨, ìĦIJ, ḻ, ãĪª, âĶ½, ðĿĻ¸, ðŁİij], [ìĿ¿, ðŁķŀ, áĴĮ, âļĤ, âĨ·, âĿ«, ðŁİij], [Ѩ, ðŁĸ¶, áĭ©, á¶ı, â½Ĺ, âºŁ, ðŁİij], [ðŁĶĢ, á¼®, à²Ł, âĻĻ, â½Ĺ, â½Ĺ, ðŁİij]]." ] }
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{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_69><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[124, 20], [158, 41], [167, 74], [166, 97], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ìĿ¿, ðŁķŀ, áĴĮ, âļĤ, âĨ·, âĿ«, ðŁİij], [Ѩ, ðŁĸ¶, áĭ©, á¶ı, â½Ĺ, âºŁ, ðŁİij], [ðŁĶĢ, á¼®, à²Ł, âĻĻ, â½Ĺ, â½Ĺ, ðŁİij], [ðŁĶĥ, ðŁĹģ, 맳, ìı±, â½Ĺ, â½Ĺ, ðŁİij]]." ] }
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{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_69><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[130, 25], [164, 48], [167, 80], [166, 97], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[Ѩ, ðŁĸ¶, áĭ©, á¶ı, â½Ĺ, âºŁ, ðŁİij], [ðŁĶĢ, á¼®, à²Ł, âĻĻ, â½Ĺ, â½Ĺ, ðŁİij], [ðŁĶĥ, ðŁĹģ, 맳, ìı±, â½Ĺ, â½Ĺ, ðŁİij], [ê¸ij, ðŁĸij, áĪĬ, ðŁķĭ, â°Ł, â½Ĺ, ðŁİij]]." ] }
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{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_8><DEPTH_75><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_8><DEPTH_56><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[138, 31], [166, 56], [167, 80], [166, 97], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ðŁĶĢ, á¼®, à²Ł, âĻĻ, â½Ĺ, â½Ĺ, ðŁİij], [ðŁĶĥ, ðŁĹģ, 맳, ìı±, â½Ĺ, â½Ĺ, ðŁİij], [ê¸ij, ðŁĸij, áĪĬ, ðŁķĭ, â°Ł, â½Ĺ, ðŁİij], [á¿IJ, ðŁĶ¤, áīIJ, á©ĭ, ðŁħĵ, âŀ¶, ðŁİij]]." ] }
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{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_69><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[148, 35], [167, 63], [167, 84], [166, 102], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ðŁĶĥ, ðŁĹģ, 맳, ìı±, â½Ĺ, â½Ĺ, ðŁİij], [ê¸ij, ðŁĸij, áĪĬ, ðŁķĭ, â°Ł, â½Ĺ, ðŁİij], [á¿IJ, ðŁĶ¤, áīIJ, á©ĭ, ðŁħĵ, âŀ¶, ðŁİij], [íĵŃ, ðŁĶ¤, Êĵ, áīIJ, ðĿĻ¸, ãĦĵ, ðŁİij]]." ] }
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{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_69><DEPTH_101><DEPTH_75><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[158, 41], [167, 63], [167, 84], [166, 102], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ê¸ij, ðŁĸij, áĪĬ, ðŁķĭ, â°Ł, â½Ĺ, ðŁİij], [á¿IJ, ðŁĶ¤, áīIJ, á©ĭ, ðŁħĵ, âŀ¶, ðŁİij], [íĵŃ, ðŁĶ¤, Êĵ, áīIJ, ðĿĻ¸, ãĦĵ, ðŁİij], [íĽľ, á¡Į, áīIJ, íķ®, ðĿĺı, ÆĪ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_69><DEPTH_56><DEPTH_75><DEPTH_75><DEPTH_8><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[164, 48], [167, 69], [167, 88], [166, 102], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[á¿IJ, ðŁĶ¤, áīIJ, á©ĭ, ðŁħĵ, âŀ¶, ðŁİij], [íĵŃ, ðŁĶ¤, Êĵ, áīIJ, ðĿĻ¸, ãĦĵ, ðŁİij], [íĽľ, á¡Į, áīIJ, íķ®, ðĿĺı, ÆĪ, ðŁİij], [ï¤ħ, ë¯ij, áĪ¨, áĪĬ, ðĿĻº, ðĿķ±, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_8><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[166, 56], [167, 74], [167, 88], [166, 102], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[íĵŃ, ðŁĶ¤, Êĵ, áīIJ, ðĿĻ¸, ãĦĵ, ðŁİij], [íĽľ, á¡Į, áīIJ, íķ®, ðĿĺı, ÆĪ, ðŁİij], [ï¤ħ, ë¯ij, áĪ¨, áĪĬ, ðĿĻº, ðĿķ±, ðŁİij], [ï¤Ĩ, ë¯ij, ìĦIJ, Êĵ, ðĿĵ¾, ðĿķ¡, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[167, 63], [167, 80], [166, 92], [166, 106], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[íĽľ, á¡Į, áīIJ, íķ®, ðĿĺı, ÆĪ, ðŁİij], [ï¤ħ, ë¯ij, áĪ¨, áĪĬ, ðĿĻº, ðĿķ±, ðŁİij], [ï¤Ĩ, ë¯ij, ìĦIJ, Êĵ, ðĿĵ¾, ðĿķ¡, ðŁİij], [ï¦ĥ, ìĭ², ìĦIJ, Éĺ, íĵ¬, ðĸ¥¨, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[167, 69], [167, 80], [166, 92], [166, 106], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¤ħ, ë¯ij, áĪ¨, áĪĬ, ðĿĻº, ðĿķ±, ðŁİij], [ï¤Ĩ, ë¯ij, ìĦIJ, Êĵ, ðĿĵ¾, ðĿķ¡, ðŁİij], [ï¦ĥ, ìĭ², ìĦIJ, Éĺ, íĵ¬, ðĸ¥¨, ðŁİij], [ï¦ĥ, à½ħ, íķ®, áĪ¨, ðĿĵķ, íĭį, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[167, 74], [167, 84], [166, 97], [166, 106], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¤Ĩ, ë¯ij, ìĦIJ, Êĵ, ðĿĵ¾, ðĿķ¡, ðŁİij], [ï¦ĥ, ìĭ², ìĦIJ, Éĺ, íĵ¬, ðĸ¥¨, ðŁİij], [ï¦ĥ, à½ħ, íķ®, áĪ¨, ðĿĵķ, íĭį, ðŁİij], [ï¦ĥ, ï®ı, ìı±, áĪĬ, ðĿĺIJ, ḻ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[167, 80], [167, 88], [166, 97], [166, 106], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¦ĥ, ìĭ², ìĦIJ, Éĺ, íĵ¬, ðĸ¥¨, ðŁİij], [ï¦ĥ, à½ħ, íķ®, áĪ¨, ðĿĵķ, íĭį, ðŁİij], [ï¦ĥ, ï®ı, ìı±, áĪĬ, ðĿĺIJ, ḻ, ðŁİij], [ï¤Ĩ, ê¸ij, Ḱ, ï®ı, 롼, áłĦ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_8><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[167, 84], [166, 92], [166, 102], [164, 110], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¦ĥ, à½ħ, íķ®, áĪ¨, ðĿĵķ, íĭį, ðŁİij], [ï¦ĥ, ï®ı, ìı±, áĪĬ, ðĿĺIJ, ḻ, ðŁİij], [ï¤Ĩ, ê¸ij, Ḱ, ï®ı, 롼, áłĦ, ðŁİij], [ï¤ħ, ë¯ij, âĦ¬, áĮĥ, áłĦ, á¨ģ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[167, 88], [166, 92], [166, 102], [164, 110], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¦ĥ, ï®ı, ìı±, áĪĬ, ðĿĺIJ, ḻ, ðŁİij], [ï¤Ĩ, ê¸ij, Ḱ, ï®ı, 롼, áłĦ, ðŁİij], [ï¤ħ, ë¯ij, âĦ¬, áĮĥ, áłĦ, á¨ģ, ðŁİij], [ï¦ĥ, ìĿ©, Úİ, á¼®, à°³, áī¨, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_69><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[166, 92], [166, 97], [166, 106], [164, 110], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¤Ĩ, ê¸ij, Ḱ, ï®ı, 롼, áłĦ, ðŁİij], [ï¤ħ, ë¯ij, âĦ¬, áĮĥ, áłĦ, á¨ģ, ðŁİij], [ï¦ĥ, ìĿ©, Úİ, á¼®, à°³, áī¨, ðŁİij], [ï¤Ĩ, ìŤ, á½Ŀ, ï®ı, Ý¥, áIJ§, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_26><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_69><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_69><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[166, 97], [166, 102], [166, 106], [164, 110], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¤ħ, ë¯ij, âĦ¬, áĮĥ, áłĦ, á¨ģ, ðŁİij], [ï¦ĥ, ìĿ©, Úİ, á¼®, à°³, áī¨, ðŁİij], [ï¤Ĩ, ìŤ, á½Ŀ, ï®ı, Ý¥, áIJ§, ðŁİij], [ï¤Ĩ, áĮ½, ðŁĨĴ, âļĤ, Ý¥, áĮĶ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_26><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_26><DEPTH_26><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_69><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_69><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[166, 102], [166, 106], [164, 110], [164, 113], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¦ĥ, ìĿ©, Úİ, á¼®, à°³, áī¨, ðŁİij], [ï¤Ĩ, ìŤ, á½Ŀ, ï®ı, Ý¥, áIJ§, ðŁİij], [ï¤Ĩ, áĮ½, ðŁĨĴ, âļĤ, Ý¥, áĮĶ, ðŁİij], [里, íĽľ, ë±ĭ, ìľ¾, Ý¥, à²Ł, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_69><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_26><DEPTH_26><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_69><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[166, 106], [164, 110], [164, 113], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¤Ĩ, ìŤ, á½Ŀ, ï®ı, Ý¥, áIJ§, ðŁİij], [ï¤Ĩ, áĮ½, ðŁĨĴ, âļĤ, Ý¥, áĮĶ, ðŁİij], [里, íĽľ, ë±ĭ, ìľ¾, Ý¥, à²Ł, ðŁİij], [ï¨Ĥ, ðIJŃĵ, ì»ģ, â¡¢, ÔĪ, à¤ij, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_26><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_69><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[164, 110], [164, 113], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¤Ĩ, áĮ½, ðŁĨĴ, âļĤ, Ý¥, áĮĶ, ðŁİij], [里, íĽľ, ë±ĭ, ìľ¾, Ý¥, à²Ł, ðŁİij], [ï¨Ĥ, ðIJŃĵ, ì»ģ, â¡¢, ÔĪ, à¤ij, Ý¥], [里, Ýĵ, áĶħ, ॱ, Ȳ, à¬ī, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_75><DEPTH_69><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_69><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[164, 113], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[里, íĽľ, ë±ĭ, ìľ¾, Ý¥, à²Ł, ðŁİij], [ï¨Ĥ, ðIJŃĵ, ì»ģ, â¡¢, ÔĪ, à¤ij, Ý¥], [里, Ýĵ, áĶħ, ॱ, Ȳ, à¬ī, Ý¥], [ðIJŃĵ, âĽı, ⽺, â°ł, ÔĪ, à°³, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_75><DEPTH_69><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¨Ĥ, ðIJŃĵ, ì»ģ, â¡¢, ÔĪ, à¤ij, Ý¥], [里, Ýĵ, áĶħ, ॱ, Ȳ, à¬ī, Ý¥], [ðIJŃĵ, âĽı, ⽺, â°ł, ÔĪ, à°³, Ý¥], [ðĿĻľ, ï¨Ĭ, â½Ĺ, â½Ĺ, ʶ, à¤ij, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_69><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[166, 113], [166, 86], [138, 62], [84, 58], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[里, Ýĵ, áĶħ, ॱ, Ȳ, à¬ī, Ý¥], [ðIJŃĵ, âĽı, ⽺, â°ł, ÔĪ, à°³, Ý¥], [ðĿĻľ, ï¨Ĭ, â½Ĺ, â½Ĺ, ʶ, à¤ij, Ý¥], [áĴĮ, âĨ¤, â°Ł, â°¡, ì»ģ, ì»ģ, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_75><DEPTH_69><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[166, 112], [162, 79], [126, 58], [75, 59], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ðIJŃĵ, âĽı, ⽺, â°ł, ÔĪ, à°³, Ý¥], [ðĿĻľ, ï¨Ĭ, â½Ĺ, â½Ĺ, ʶ, à¤ij, Ý¥], [áĴĮ, âĨ¤, â°Ł, â°¡, ì»ģ, ì»ģ, Ý¥], [íĭį, ðĿļı, âķĬ, àłĮ, ᳬ, áĶŃ, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_69><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_26><DEPTH_26><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_69><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_69><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_69><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[167, 105], [162, 79], [126, 58], [75, 59], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ðĿĻľ, ï¨Ĭ, â½Ĺ, â½Ĺ, ʶ, à¤ij, Ý¥], [áĴĮ, âĨ¤, â°Ł, â°¡, ì»ģ, ì»ģ, Ý¥], [íĭį, ðĿļı, âķĬ, àłĮ, ᳬ, áĶŃ, Ý¥], [ðĿĺļ, ðĿĵ¾, áļĮ, ðĿĶĵ, ìł°, ìĿ¿, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_26><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_26><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_69><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[168, 95], [156, 72], [112, 58], [75, 59], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[áĴĮ, âĨ¤, â°Ł, â°¡, ì»ģ, ì»ģ, Ý¥], [íĭį, ðĿļı, âķĬ, àłĮ, ᳬ, áĶŃ, Ý¥], [ðĿĺļ, ðĿĵ¾, áļĮ, ðĿĶĵ, ìł°, ìĿ¿, Ý¥], [áĻ³, íĭį, âĿ«, áĬĴ, ìī¡, á¼®, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_75><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[166, 86], [147, 67], [112, 58], [75, 59], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[íĭį, ðĿļı, âķĬ, àłĮ, ᳬ, áĶŃ, Ý¥], [ðĿĺļ, ðĿĵ¾, áļĮ, ðĿĶĵ, ìł°, ìĿ¿, Ý¥], [áĻ³, íĭį, âĿ«, áĬĴ, ìī¡, á¼®, Ý¥], [âĶ½, âļ£, âĿ«, Úİ, ìŤ, ðŁĶ¤, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_26><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_75><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[162, 79], [138, 62], [97, 58], [67, 58], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ðĿĺļ, ðĿĵ¾, áļĮ, ðĿĶĵ, ìł°, ìĿ¿, Ý¥], [áĻ³, íĭį, âĿ«, áĬĴ, ìī¡, á¼®, Ý¥], [âĶ½, âļ£, âĿ«, Úİ, ìŤ, ðŁĶ¤, Ý¥], [ãī«, ḻ, ðĿĻ¸, ìī¡, ê¸ij, ðŁĶ¢, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_75><DEPTH_32><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_69><DEPTH_56><DEPTH_26><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[156, 72], [138, 62], [97, 58], [67, 58], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[áĻ³, íĭį, âĿ«, áĬĴ, ìī¡, á¼®, Ý¥], [âĶ½, âļ£, âĿ«, Úİ, ìŤ, ðŁĶ¤, Ý¥], [ãī«, ḻ, ðĿĻ¸, ìī¡, ê¸ij, ðŁĶ¢, Ý¥], [âĴ¡, ðĿĵ°, âĻĻ, âļ§, ðŁķŀ, ðŁĶĢ, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_75><DEPTH_75><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_9><DEPTH_75><DEPTH_26><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_8><DEPTH_75><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[147, 67], [126, 58], [84, 58], [67, 58], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[âĶ½, âļ£, âĿ«, Úİ, ìŤ, ðŁĶ¤, Ý¥], [ãī«, ḻ, ðĿĻ¸, ìī¡, ê¸ij, ðŁĶ¢, Ý¥], [âĴ¡, ðĿĵ°, âĻĻ, âļ§, ðŁķŀ, ðŁĶĢ, Ý¥], [â¼Ń, ðĿĻľ, ì¥ī, ë»ħ, ðŁı¯, ðŁĶĢ, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_69><DEPTH_101><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_26><DEPTH_75><DEPTH_125><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_69><DEPTH_26><DEPTH_26><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_8><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[138, 62], [112, 58], [84, 58], [67, 58], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ãī«, ḻ, ðĿĻ¸, ìī¡, ê¸ij, ðŁĶ¢, Ý¥], [âĴ¡, ðĿĵ°, âĻĻ, âļ§, ðŁķŀ, ðŁĶĢ, Ý¥], [â¼Ń, ðĿĻľ, ì¥ī, ë»ħ, ðŁı¯, ðŁĶĢ, Ý¥], [â°ij, Ýĵ, ìĦIJ, ðŁħ±, ðŁİij, ðŁķĿ, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_69><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_56><DEPTH_30><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_75><DEPTH_26><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_8><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[126, 58], [97, 58], [75, 59], [64, 59], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[âĴ¡, ðĿĵ°, âĻĻ, âļ§, ðŁķŀ, ðŁĶĢ, Ý¥], [â¼Ń, ðĿĻľ, ì¥ī, ë»ħ, ðŁı¯, ðŁĶĢ, Ý¥], [â°ij, Ýĵ, ìĦIJ, ðŁħ±, ðŁİij, ðŁķĿ, Ý¥], [ãī«, à¤ij, ëĶī, ìł°, ðŁĶĩ, êĻĭ, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_69><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_75><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_69><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_32><DEPTH_75><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[112, 58], [97, 58], [75, 59], [64, 59], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[â¼Ń, ðĿĻľ, ì¥ī, ë»ħ, ðŁı¯, ðŁĶĢ, Ý¥], [â°ij, Ýĵ, ìĦIJ, ðŁħ±, ðŁİij, ðŁķĿ, Ý¥], [ãī«, à¤ij, ëĶī, ìł°, ðŁĶĩ, êĻĭ, Ý¥], [ðĿĺIJ, Ý¥, ìł°, á¶ı, ⼤, áĪĬ, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_8><DEPTH_75><DEPTH_101><DEPTH_75><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_26><DEPTH_26><DEPTH_101><DEPTH_69><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[97, 58], [84, 58], [67, 58], [64, 59], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[â°ij, Ýĵ, ìĦIJ, ðŁħ±, ðŁİij, ðŁķĿ, Ý¥], [ãī«, à¤ij, ëĶī, ìł°, ðŁĶĩ, êĻĭ, Ý¥], [ðĿĺIJ, Ý¥, ìł°, á¶ı, ⼤, áĪĬ, Ý¥], [âĶŀ, ðĿĵ°, áĪ¨, á¶ı, ᨸ, ì»ģ, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_8><DEPTH_26><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_75><DEPTH_26><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_26><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_69><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[84, 58], [75, 59], [67, 58], [64, 59], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ãī«, à¤ij, ëĶī, ìł°, ðŁĶĩ, êĻĭ, Ý¥], [ðĿĺIJ, Ý¥, ìł°, á¶ı, ⼤, áĪĬ, Ý¥], [âĶŀ, ðĿĵ°, áĪ¨, á¶ı, ᨸ, ì»ģ, Ý¥], [ðĿĵŀ, á¿ļ, áĪĬ, íĬŃ, ðĿĵŀ, à°µ, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_8><DEPTH_26><DEPTH_101><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_75><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_26><DEPTH_26><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[75, 59], [67, 58], [64, 59], [64, 61], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ðĿĺIJ, Ý¥, ìł°, á¶ı, ⼤, áĪĬ, Ý¥], [âĶŀ, ðĿĵ°, áĪ¨, á¶ı, ᨸ, ì»ģ, Ý¥], [ðĿĵŀ, á¿ļ, áĪĬ, íĬŃ, ðĿĵŀ, à°µ, Ý¥], [里, ìĽ¢, á¶ı, à´´, ʶ, à²Ł, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_26><DEPTH_101><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_26><DEPTH_26><DEPTH_101><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[67, 58], [64, 59], [64, 61], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[âĶŀ, ðĿĵ°, áĪ¨, á¶ı, ᨸ, ì»ģ, Ý¥], [ðĿĵŀ, á¿ļ, áĪĬ, íĬŃ, ðĿĵŀ, à°µ, Ý¥], [里, ìĽ¢, á¶ı, à´´, ʶ, à²Ł, Ý¥], [ï¨Ĥ, ðIJŃĵ, ì»ģ, â¡¢, ÔĪ, à¤ij, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_26><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_101><DEPTH_101><DEPTH_69><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[64, 59], [64, 61], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ðĿĵŀ, á¿ļ, áĪĬ, íĬŃ, ðĿĵŀ, à°µ, Ý¥], [里, ìĽ¢, á¶ı, à´´, ʶ, à²Ł, Ý¥], [ï¨Ĥ, ðIJŃĵ, ì»ģ, â¡¢, ÔĪ, à¤ij, ðŁİij], [ìľ¾, à¤ij, ï¥ł, áĶĬ, ÔĪ, ʶ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_26><DEPTH_101><DEPTH_30><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_101><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[64, 61], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[里, ìĽ¢, á¶ı, à´´, ʶ, à²Ł, Ý¥], [ï¨Ĥ, ðIJŃĵ, ì»ģ, â¡¢, ÔĪ, à¤ij, ðŁİij], [ìľ¾, à¤ij, ï¥ł, áĶĬ, ÔĪ, ʶ, ðŁİij], [ìŤ, ðĿļģ, íĵ¶, áĭ©, 맳, à°µ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_26><DEPTH_101><DEPTH_30><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_26><DEPTH_26><DEPTH_101><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_75><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¨Ĥ, ðIJŃĵ, ì»ģ, â¡¢, ÔĪ, à¤ij, ðŁİij], [ìľ¾, à¤ij, ï¥ł, áĶĬ, ÔĪ, ʶ, ðŁİij], [ìŤ, ðĿļģ, íĵ¶, áĭ©, 맳, à°µ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_69><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[119, 17], [150, 36], [167, 70], [166, 93], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[⤦, â¡¢, à²Ł, áĶĬ, ðĿķį, ïŃ², ðŁİij], [à½ħ, ê¸ij, á¨ģ, áĴĮ, âĽı, ðĿĻ½, ðŁİij], [ìĭ², ðŁķŀ, áIJ§, á©ĭ, âĶ², ê¦Ķ, ðŁİij], [ðŁĸĩ, ðŁĹģ, áĮĦ, á¿ļ, â½Ĺ, â½Ĺ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_75><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_69><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[121, 17], [159, 43], [167, 75], [166, 98], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[à½ħ, ê¸ij, á¨ģ, áĴĮ, âĽı, ðĿĻ½, ðŁİij], [ìĭ², ðŁķŀ, áIJ§, á©ĭ, âĶ², ê¦Ķ, ðŁİij], [ðŁĸĩ, ðŁĹģ, áĮĦ, á¿ļ, â½Ĺ, â½Ĺ, ðŁİij], [ðŁĵª, áºĢ, à¤ij, á©ĭ, â½Ĺ, â½Ĺ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_69><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[126, 21], [159, 43], [167, 75], [166, 98], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ìĭ², ðŁķŀ, áIJ§, á©ĭ, âĶ², ê¦Ķ, ðŁİij], [ðŁĸĩ, ðŁĹģ, áĮĦ, á¿ļ, â½Ĺ, â½Ĺ, ðŁİij], [ðŁĵª, áºĢ, à¤ij, á©ĭ, â½Ĺ, â½Ĺ, ðŁİij], [ðŁĶ¢, Ѩ, â¡¢, ìı±, â½Ĺ, â½Ĺ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_69><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[131, 26], [164, 50], [167, 81], [166, 98], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ðŁĸĩ, ðŁĹģ, áĮĦ, á¿ļ, â½Ĺ, â½Ĺ, ðŁİij], [ðŁĵª, áºĢ, à¤ij, á©ĭ, â½Ĺ, â½Ĺ, ðŁİij], [ðŁĶ¢, Ѩ, â¡¢, ìı±, â½Ĺ, â½Ĺ, ðŁİij], [ìī¡, ðŁķ¢, áĨº, à´´, 嘆, â½Ĺ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_8><DEPTH_75><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_69><DEPTH_56><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[139, 31], [167, 57], [167, 81], [166, 98], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ðŁĵª, áºĢ, à¤ij, á©ĭ, â½Ĺ, â½Ĺ, ðŁİij], [ðŁĶ¢, Ѩ, â¡¢, ìı±, â½Ĺ, â½Ĺ, ðŁİij], [ìī¡, ðŁķ¢, áĨº, à´´, 嘆, â½Ĺ, ðŁİij], [â¡¢, ðŁĶ¤, áĮĥ, á¾Ķ, ðĿļı, âĩŀ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_8><DEPTH_101><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_69><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[150, 36], [167, 64], [167, 85], [166, 103], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ðŁĶ¢, Ѩ, â¡¢, ìı±, â½Ĺ, â½Ĺ, ðŁİij], [ìī¡, ðŁķ¢, áĨº, à´´, 嘆, â½Ĺ, ðŁİij], [â¡¢, ðŁĶ¤, áĮĥ, á¾Ķ, ðĿļı, âĩŀ, ðŁİij], [íķ±, ðŁĶ©, ï®ı, Õĥ, ðĿĻ°, âĿ«, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_69><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_8><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[159, 43], [167, 64], [167, 85], [166, 103], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ìī¡, ðŁķ¢, áĨº, à´´, 嘆, â½Ĺ, ðŁİij], [â¡¢, ðŁĶ¤, áĮĥ, á¾Ķ, ðĿļı, âĩŀ, ðŁİij], [íķ±, ðŁĶ©, ï®ı, Õĥ, ðĿĻ°, âĿ«, ðŁİij], [ï¤ħ, âĦ¬, áĮ½, ðŁħ±, ðĿĺı, ðĿļı, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_56><DEPTH_75><DEPTH_75><DEPTH_8><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[164, 50], [167, 70], [167, 88], [166, 103], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[â¡¢, ðŁĶ¤, áĮĥ, á¾Ķ, ðĿļı, âĩŀ, ðŁİij], [íķ±, ðŁĶ©, ï®ı, Õĥ, ðĿĻ°, âĿ«, ðŁİij], [ï¤ħ, âĦ¬, áĮ½, ðŁħ±, ðĿĺı, ðĿļı, ðŁİij], [ï¤ħ, ë¯ij, áĪĬ, áīIJ, ðĿĻº, ðĿķ¡, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_26><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_8><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[167, 57], [167, 75], [167, 88], [166, 103], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[íķ±, ðŁĶ©, ï®ı, Õĥ, ðĿĻ°, âĿ«, ðŁİij], [ï¤ħ, âĦ¬, áĮ½, ðŁħ±, ðĿĺı, ðĿļı, ðŁİij], [ï¤ħ, ë¯ij, áĪĬ, áīIJ, ðĿĻº, ðĿķ¡, ðŁİij], [ï¤Ĩ, ë¯ij, ë¯ij, ðŁĨĴ, ðĿij³, ðĿķį, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[167, 64], [167, 81], [166, 93], [166, 107], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¤ħ, âĦ¬, áĮ½, ðŁħ±, ðĿĺı, ðĿļı, ðŁİij], [ï¤ħ, ë¯ij, áĪĬ, áīIJ, ðĿĻº, ðĿķ¡, ðŁİij], [ï¤Ĩ, ë¯ij, ë¯ij, ðŁĨĴ, ðĿij³, ðĿķį, ðŁİij], [ï¦ĥ, ìĿ©, ìĭ², Õĥ, íĵ¬, ðĸ¥, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_69><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[167, 70], [167, 81], [166, 93], [166, 107], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¤ħ, ë¯ij, áĪĬ, áīIJ, ðĿĻº, ðĿķ¡, ðŁİij], [ï¤Ĩ, ë¯ij, ë¯ij, ðŁĨĴ, ðĿij³, ðĿķį, ðŁİij], [ï¦ĥ, ìĿ©, ìĭ², Õĥ, íĵ¬, ðĸ¥, ðŁİij], [ï¦ĥ, áĨº, ï®ı, áĪ¾, ðĿķ±, 롼, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_69><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[167, 75], [167, 85], [166, 98], [166, 107], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¤Ĩ, ë¯ij, ë¯ij, ðŁĨĴ, ðĿij³, ðĿķį, ðŁİij], [ï¦ĥ, ìĿ©, ìĭ², Õĥ, íĵ¬, ðĸ¥, ðŁİij], [ï¦ĥ, áĨº, ï®ı, áĪ¾, ðĿķ±, 롼, ðŁİij], [ï¦ĥ, ì¥ī, ë¼Ŀ, Õĥ, ðĿĶĵ, á¨ģ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[167, 81], [167, 88], [166, 98], [166, 107], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¦ĥ, ìĿ©, ìĭ², Õĥ, íĵ¬, ðĸ¥, ðŁİij], [ï¦ĥ, áĨº, ï®ı, áĪ¾, ðĿķ±, 롼, ðŁİij], [ï¦ĥ, ì¥ī, ë¼Ŀ, Õĥ, ðĿĶĵ, á¨ģ, ðŁİij], [ï¤Ĩ, 㳬, á¡Į, ðŁķĭ, ê²Ļ, á¨ģ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_30><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_75><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[167, 85], [166, 93], [166, 103], [164, 111], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¦ĥ, áĨº, ï®ı, áĪ¾, ðĿķ±, 롼, ðŁİij], [ï¦ĥ, ì¥ī, ë¼Ŀ, Õĥ, ðĿĶĵ, á¨ģ, ðŁİij], [ï¤Ĩ, 㳬, á¡Į, ðŁķĭ, ê²Ļ, á¨ģ, ðŁİij], [ï¤ħ, ë¼Ŀ, âĦ¬, áĪĬ, áĴĮ, áłĦ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_75><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_75><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[167, 88], [166, 93], [166, 103], [164, 111], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¦ĥ, ì¥ī, ë¼Ŀ, Õĥ, ðĿĶĵ, á¨ģ, ðŁİij], [ï¤Ĩ, 㳬, á¡Į, ðŁķĭ, ê²Ļ, á¨ģ, ðŁİij], [ï¤ħ, ë¼Ŀ, âĦ¬, áĪĬ, áĴĮ, áłĦ, ðŁİij], [ï¦ĥ, ìŤ, ðŁĹģ, Ḱ, à¬ī, áģ¼, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_75><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_8><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_69><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[166, 93], [166, 98], [166, 107], [164, 111], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¤Ĩ, 㳬, á¡Į, ðŁķĭ, ê²Ļ, á¨ģ, ðŁİij], [ï¤ħ, ë¼Ŀ, âĦ¬, áĪĬ, áĴĮ, áłĦ, ðŁİij], [ï¦ĥ, ìŤ, ðŁĹģ, Ḱ, à¬ī, áģ¼, ðŁİij], [ï¤Ĩ, ìĿ¿, 㳬, áĨº, Ýĵ, áĶħ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_26><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_26><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[166, 98], [166, 103], [166, 107], [164, 111], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¤ħ, ë¼Ŀ, âĦ¬, áĪĬ, áĴĮ, áłĦ, ðŁİij], [ï¦ĥ, ìŤ, ðŁĹģ, Ḱ, à¬ī, áģ¼, ðŁİij], [ï¤Ĩ, ìĿ¿, 㳬, áĨº, Ýĵ, áĶħ, ðŁİij], [ï¦ĥ, á¶ı, à´´, âĻĻ, Ý¥, áĭ©, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_26><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_26><DEPTH_26><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_69><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_69><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[166, 103], [166, 107], [164, 111], [164, 113], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¦ĥ, ìŤ, ðŁĹģ, Ḱ, à¬ī, áģ¼, ðŁİij], [ï¤Ĩ, ìĿ¿, 㳬, áĨº, Ýĵ, áĶħ, ðŁİij], [ï¦ĥ, á¶ı, à´´, âĻĻ, Ý¥, áĭ©, ðŁİij], [里, ï¦ĥ, ì³ħ, ì»ģ, Ý¥, à°µ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_69><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_69><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_69><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[166, 107], [164, 111], [164, 113], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¤Ĩ, ìĿ¿, 㳬, áĨº, Ýĵ, áĶħ, ðŁİij], [ï¦ĥ, á¶ı, à´´, âĻĻ, Ý¥, áĭ©, ðŁİij], [里, ï¦ĥ, ì³ħ, ì»ģ, Ý¥, à°µ, ðŁİij], [ï¨Ĥ, ðIJŃĵ, ì»ģ, â¡¢, ÔĪ, à¤ij, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_26><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_69><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_69><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[164, 111], [164, 113], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¦ĥ, á¶ı, à´´, âĻĻ, Ý¥, áĭ©, ðŁİij], [里, ï¦ĥ, ì³ħ, ì»ģ, Ý¥, à°µ, ðŁİij], [ï¨Ĥ, ðIJŃĵ, ì»ģ, â¡¢, ÔĪ, à¤ij, Ý¥], [里, áĢı, ê³Ĥ, áĴĮ, ðŁıĩ, à¬ī, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_75><DEPTH_69><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[164, 113], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[里, ï¦ĥ, ì³ħ, ì»ģ, Ý¥, à°µ, ðŁİij], [ï¨Ĥ, ðIJŃĵ, ì»ģ, â¡¢, ÔĪ, à¤ij, Ý¥], [里, áĢı, ê³Ĥ, áĴĮ, ðŁıĩ, à¬ī, Ý¥], [ðIJŃĵ, á¥Ķ, â½Ĺ, â½Ĺ, Ôij, à°³, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_75><DEPTH_69><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¨Ĥ, ðIJŃĵ, ì»ģ, â¡¢, ÔĪ, à¤ij, Ý¥], [里, áĢı, ê³Ĥ, áĴĮ, ðŁıĩ, à¬ī, Ý¥], [ðIJŃĵ, á¥Ķ, â½Ĺ, â½Ĺ, Ôij, à°³, Ý¥], [ðŁħ¢, ãĩº, â½Ĺ, â½Ĺ, ðŁıĩ, Ýĵ, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_75><DEPTH_69><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[166, 113], [166, 85], [136, 61], [83, 58], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[里, áĢı, ê³Ĥ, áĴĮ, ðŁıĩ, à¬ī, Ý¥], [ðIJŃĵ, á¥Ķ, â½Ĺ, â½Ĺ, Ôij, à°³, Ý¥], [ðŁħ¢, ãĩº, â½Ĺ, â½Ĺ, ðŁıĩ, Ýĵ, Ý¥], [áłĦ, ãī«, âĢ´, âĴ¡, ìľ¾, ë±IJ, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_75><DEPTH_69><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[166, 111], [162, 78], [124, 57], [74, 58], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ðIJŃĵ, á¥Ķ, â½Ĺ, â½Ĺ, Ôij, à°³, Ý¥], [ðŁħ¢, ãĩº, â½Ĺ, â½Ĺ, ðŁıĩ, Ýĵ, Ý¥], [áłĦ, ãī«, âĢ´, âĴ¡, ìľ¾, ë±IJ, Ý¥], [íĵ¶, ðĿĻ¿, á¤Ĭ, ðĿļı, áĮ½, áīIJ, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_69><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_26><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_26><DEPTH_26><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_69><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_69><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[167, 104], [162, 78], [124, 57], [74, 58], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ðŁħ¢, ãĩº, â½Ĺ, â½Ĺ, ðŁıĩ, Ýĵ, Ý¥], [áłĦ, ãī«, âĢ´, âĴ¡, ìľ¾, ë±IJ, Ý¥], [íĵ¶, ðĿĻ¿, á¤Ĭ, ðĿļı, áĮ½, áīIJ, Ý¥], [ðĿĻ¸, ðĿij³, á¸Ĭ, ðĿĵ¾, ìĿ©, ìĭ², Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_75><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_69><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_69><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[167, 93], [155, 72], [111, 58], [74, 58], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[áłĦ, ãī«, âĢ´, âĴ¡, ìľ¾, ë±IJ, Ý¥], [íĵ¶, ðĿĻ¿, á¤Ĭ, ðĿļı, áĮ½, áīIJ, Ý¥], [ðĿĻ¸, ðĿij³, á¸Ĭ, ðĿĵ¾, ìĿ©, ìĭ², Ý¥], [áļĮ, ì¼ĩ, âĿª, ʶ, ìĭ², á¡Į, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_75><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_75><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[166, 85], [146, 66], [111, 58], [74, 58], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[íĵ¶, ðĿĻ¿, á¤Ĭ, ðĿļı, áĮ½, áīIJ, Ý¥], [ðĿĻ¸, ðĿij³, á¸Ĭ, ðĿĵ¾, ìĿ©, ìĭ², Ý¥], [áļĮ, ì¼ĩ, âĿª, ʶ, ìĭ², á¡Į, Ý¥], [âķĬ, âļ£, âĽı, ðŁķŀ, ìŤ, ðŁĶ¢, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_26><DEPTH_26><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_26><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_75><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[162, 78], [136, 61], [96, 58], [67, 58], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ðĿĻ¸, ðĿij³, á¸Ĭ, ðĿĵ¾, ìĿ©, ìĭ², Ý¥], [áļĮ, ì¼ĩ, âĿª, ʶ, ìĭ², á¡Į, Ý¥], [âķĬ, âļ£, âĽı, ðŁķŀ, ìŤ, ðŁĶ¢, Ý¥], [àºĤ, áłĦ, ðĿĵ¾, ì¥ī, 㳬, ðŁĶ¢, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_32><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_69><DEPTH_101><DEPTH_26><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_75><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[155, 72], [136, 61], [96, 58], [67, 58], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[áļĮ, ì¼ĩ, âĿª, ʶ, ìĭ², á¡Į, Ý¥], [âķĬ, âļ£, âĽı, ðŁķŀ, ìŤ, ðŁĶ¢, Ý¥], [àºĤ, áłĦ, ðĿĵ¾, ì¥ī, 㳬, ðŁĶ¢, Ý¥], [â°į, ðIJ°¼, áĮ½, á½Ŀ, ðŁķļ, ðŁĵ¼, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_8><DEPTH_101><DEPTH_75><DEPTH_75><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_125><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_9><DEPTH_75><DEPTH_26><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_8><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[146, 66], [124, 57], [83, 58], [67, 58], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[âķĬ, âļ£, âĽı, ðŁķŀ, ìŤ, ðŁĶ¢, Ý¥], [àºĤ, áłĦ, ðĿĵ¾, ì¥ī, 㳬, ðŁĶ¢, Ý¥], [â°į, ðIJ°¼, áĮ½, á½Ŀ, ðŁķļ, ðŁĵ¼, Ý¥], [ãĬ¥, ðĿļģ, ìł°, ìĭ², ðŁıı, ðŁĶĤ, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_69><DEPTH_101><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_26><DEPTH_101><DEPTH_30><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_26><DEPTH_26><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[136, 61], [111, 58], [83, 58], [67, 58], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[àºĤ, áłĦ, ðĿĵ¾, ì¥ī, 㳬, ðŁĶ¢, Ý¥], [â°į, ðIJ°¼, áĮ½, á½Ŀ, ðŁķļ, ðŁĵ¼, Ý¥], [ãĬ¥, ðĿļģ, ìł°, ìĭ², ðŁıı, ðŁĶĤ, Ý¥], [â°į, Ýĵ, ë»ħ, íĬŃ, ðŁİ¿, ðŁķ¢, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_69><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_26><DEPTH_56><DEPTH_30><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_69><DEPTH_101><DEPTH_75><DEPTH_26><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_8><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[124, 57], [96, 58], [74, 58], [64, 59], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[â°į, ðIJ°¼, áĮ½, á½Ŀ, ðŁķļ, ðŁĵ¼, Ý¥], [ãĬ¥, ðĿļģ, ìł°, ìĭ², ðŁıı, ðŁĶĤ, Ý¥], [â°į, Ýĵ, ë»ħ, íĬŃ, ðŁİ¿, ðŁķ¢, Ý¥], [ãĦį, ॱ, ëĹį, ï®ı, ðŁĶ©, ëĹį, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_75><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_69><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_8><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[111, 58], [96, 58], [74, 58], [64, 59], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ãĬ¥, ðĿļģ, ìł°, ìĭ², ðŁıı, ðŁĶĤ, Ý¥], [â°į, Ýĵ, ë»ħ, íĬŃ, ðŁİ¿, ðŁķ¢, Ý¥], [ãĦį, ॱ, ëĹį, ï®ı, ðŁĶ©, ëĹį, Ý¥], [ðĿķį, Ýĵ, íĬŃ, á¾Ķ, ëĹį, áĮĥ, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_8><DEPTH_26><DEPTH_101><DEPTH_75><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_101><DEPTH_69><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[96, 58], [83, 58], [67, 58], [64, 59], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[â°į, Ýĵ, ë»ħ, íĬŃ, ðŁİ¿, ðŁķ¢, Ý¥], [ãĦį, ॱ, ëĹį, ï®ı, ðŁĶ©, ëĹį, Ý¥], [ðĿķį, Ýĵ, íĬŃ, á¾Ķ, ëĹį, áĮĥ, Ý¥], [áĶħ, ðIJŃĵ, áīIJ, á©ĭ, âļĤ, íķ±, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_8><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_26><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_75><DEPTH_26><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_69><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[83, 58], [74, 58], [67, 58], [64, 59], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ãĦį, ॱ, ëĹį, ï®ı, ðŁĶ©, ëĹį, Ý¥], [ðĿķį, Ýĵ, íĬŃ, á¾Ķ, ëĹį, áĮĥ, Ý¥], [áĶħ, ðIJŃĵ, áīIJ, á©ĭ, âļĤ, íķ±, Ý¥], [ðIJ¤Ķ, á©ĭ, áĨº, ìĿ¿, ðĿļģ, áģ¼, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_8><DEPTH_26><DEPTH_101><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[74, 58], [67, 58], [64, 59], [64, 62], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ðĿķį, Ýĵ, íĬŃ, á¾Ķ, ëĹį, áĮĥ, Ý¥], [áĶħ, ðIJŃĵ, áīIJ, á©ĭ, âļĤ, íķ±, Ý¥], [ðIJ¤Ķ, á©ĭ, áĨº, ìĿ¿, ðĿļģ, áģ¼, Ý¥], [里, íĵĸ, âļĤ, áĮĥ, ʶ, à°µ, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_26><DEPTH_101><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[67, 58], [64, 59], [64, 62], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[áĶħ, ðIJŃĵ, áīIJ, á©ĭ, âļĤ, íķ±, Ý¥], [ðIJ¤Ķ, á©ĭ, áĨº, ìĿ¿, ðĿļģ, áģ¼, Ý¥], [里, íĵĸ, âļĤ, áĮĥ, ʶ, à°µ, Ý¥], [ï¨Ĥ, ðIJŃĵ, ì»ģ, â¡¢, ÔĪ, à¤ij, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_26><DEPTH_101><DEPTH_30><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_101><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[64, 59], [64, 62], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ðIJ¤Ķ, á©ĭ, áĨº, ìĿ¿, ðĿļģ, áģ¼, Ý¥], [里, íĵĸ, âļĤ, áĮĥ, ʶ, à°µ, Ý¥], [ï¨Ĥ, ðIJŃĵ, ì»ģ, â¡¢, ÔĪ, à¤ij, ðŁİij], [ëĸ°, ॱ, ðĿĵ¹, âļ£, ðŁıĩ, ʶ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_26><DEPTH_101><DEPTH_30><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_101><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[64, 62], [64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[里, íĵĸ, âļĤ, áĮĥ, ʶ, à°µ, Ý¥], [ï¨Ĥ, ðIJŃĵ, ì»ģ, â¡¢, ÔĪ, à¤ij, ðŁİij], [ëĸ°, ॱ, ðĿĵ¹, âļ£, ðŁıĩ, ʶ, ðŁİij], [ë¯ij, ðĿļģ, íĭį, áĢı, âļĹ, à°µ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_26><DEPTH_101><DEPTH_30><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_26><DEPTH_26><DEPTH_101><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_75><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 1. The subtask goals are: place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[64, 63]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¨Ĥ, ðIJŃĵ, ì»ģ, â¡¢, ÔĪ, à¤ij, ðŁİij], [ëĸ°, ॱ, ðĿĵ¹, âļ£, ðŁıĩ, ʶ, ðŁİij], [ë¯ij, ðĿļģ, íĭį, áĢı, âļĹ, à°µ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_69><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[119, 17], [151, 37], [167, 71], [166, 93], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[âļĤ, á©ĭ, áĮĦ, ãĪª, ðĿĺIJ, ðĸ¥, ðŁİij], [Êĵ, á½Ŀ, á¨ģ, à²Ł, âĿ«, ðĿļĦ, ðŁİij], [ë»ħ, ðŁķŀ, áĮĦ, áĶŃ, â°İ, âĨ·, ðŁİij], [ðŁķł, á¡Į, áĮĶ, â¡¢, â½Ĺ, â½Ĺ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_75><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_69><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[121, 18], [160, 44], [167, 76], [166, 99], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[Êĵ, á½Ŀ, á¨ģ, à²Ł, âĿ«, ðĿļĦ, ðŁİij], [ë»ħ, ðŁķŀ, áĮĦ, áĶŃ, â°İ, âĨ·, ðŁİij], [ðŁķł, á¡Į, áĮĶ, â¡¢, â½Ĺ, â½Ĺ, ðŁİij], [ðŁĵª, Ḱ, ðŁıĩ, áĮĥ, â½Ĺ, â½Ĺ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_69><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[126, 22], [160, 44], [167, 76], [166, 99], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ë»ħ, ðŁķŀ, áĮĦ, áĶŃ, â°İ, âĨ·, ðŁİij], [ðŁķł, á¡Į, áĮĶ, â¡¢, â½Ĺ, â½Ĺ, ðŁİij], [ðŁĵª, Ḱ, ðŁıĩ, áĮĥ, â½Ĺ, â½Ĺ, ðŁİij], [ðŁķĸ, Ѩ, âĻĻ, ìı±, â½Ĺ, â½Ĺ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_69><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[132, 27], [166, 51], [167, 81], [166, 99], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ðŁķł, á¡Į, áĮĶ, â¡¢, â½Ĺ, â½Ĺ, ðŁİij], [ðŁĵª, Ḱ, ðŁıĩ, áĮĥ, â½Ĺ, â½Ĺ, ðŁİij], [ðŁķĸ, Ѩ, âĻĻ, ìı±, â½Ĺ, â½Ĺ, ðŁİij], [ìł°, ðŁķł, áĨº, áĪ¨, áħ£, ï¨Ĭ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_8><DEPTH_75><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_69><DEPTH_56><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[140, 32], [167, 58], [167, 81], [166, 99], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ðŁĵª, Ḱ, ðŁıĩ, áĮĥ, â½Ĺ, â½Ĺ, ðŁİij], [ðŁķĸ, Ѩ, âĻĻ, ìı±, â½Ĺ, â½Ĺ, ðŁİij], [ìł°, ðŁķł, áĨº, áĪ¨, áħ£, ï¨Ĭ, ðŁİij], [맳, ðŁĶ¢, áīIJ, á¿IJ, ðĿļĦ, âĨ¤, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_69><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[151, 37], [167, 65], [167, 86], [166, 103], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ðŁķĸ, Ѩ, âĻĻ, ìı±, â½Ĺ, â½Ĺ, ðŁİij], [ìł°, ðŁķł, áĨº, áĪ¨, áħ£, ï¨Ĭ, ðŁİij], [맳, ðŁĶ¢, áīIJ, á¿IJ, ðĿļĦ, âĨ¤, ðŁİij], [íĽľ, ðŁķļ, íķ®, ðŁħ±, ðĿĻ°, âĽı, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_69><DEPTH_101><DEPTH_75><DEPTH_101><DEPTH_8><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[160, 44], [167, 65], [167, 86], [166, 103], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ìł°, ðŁķł, áĨº, áĪ¨, áħ£, ï¨Ĭ, ðŁİij], [맳, ðŁĶ¢, áīIJ, á¿IJ, ðĿļĦ, âĨ¤, ðŁİij], [íĽľ, ðŁķļ, íķ®, ðŁħ±, ðĿĻ°, âĽı, ðŁİij], [ï¤ħ, 㳬, áĮ½, ðŁĨĴ, ðĿĺı, ðĿĻ½, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_56><DEPTH_75><DEPTH_75><DEPTH_8><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[166, 51], [167, 71], [166, 89], [166, 103], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[맳, ðŁĶ¢, áīIJ, á¿IJ, ðĿļĦ, âĨ¤, ðŁİij], [íĽľ, ðŁķļ, íķ®, ðŁħ±, ðĿĻ°, âĽı, ðŁİij], [ï¤ħ, 㳬, áĮ½, ðŁĨĴ, ðĿĺı, ðĿĻ½, ðŁİij], [ï¤ħ, ë¯ij, à½ħ, áĮ½, ðĿĻ¸, ðĿķ¡, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_26><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_26><DEPTH_26><DEPTH_8><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[167, 58], [167, 76], [166, 89], [166, 103], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[íĽľ, ðŁķļ, íķ®, ðŁħ±, ðĿĻ°, âĽı, ðŁİij], [ï¤ħ, 㳬, áĮ½, ðŁĨĴ, ðĿĺı, ðĿĻ½, ðŁİij], [ï¤ħ, ë¯ij, à½ħ, áĮ½, ðĿĻ¸, ðĿķ¡, ðŁİij], [ï¤Ĩ, ë¡ij, ë¡ij, ï®ı, ðIJŃĬ, ðĿĶĵ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[167, 65], [167, 81], [166, 93], [166, 107], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¤ħ, 㳬, áĮ½, ðŁĨĴ, ðĿĺı, ðĿĻ½, ðŁİij], [ï¤ħ, ë¯ij, à½ħ, áĮ½, ðĿĻ¸, ðĿķ¡, ðŁİij], [ï¤Ĩ, ë¡ij, ë¡ij, ï®ı, ðIJŃĬ, ðĿĶĵ, ðŁİij], [ï¦ĥ, ìŁĻ, ìŤ, à´´, íĵ¬, ðIJ±ħ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[167, 71], [167, 81], [166, 93], [166, 107], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¤ħ, ë¯ij, à½ħ, áĮ½, ðĿĻ¸, ðĿķ¡, ðŁİij], [ï¤Ĩ, ë¡ij, ë¡ij, ï®ı, ðIJŃĬ, ðĿĶĵ, ðŁİij], [ï¦ĥ, ìŁĻ, ìŤ, à´´, íĵ¬, ðIJ±ħ, ðŁİij], [ï¦ĥ, áĨº, ï®ı, áīIJ, ðĿĺļ, ê²´, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_75><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_8><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[167, 76], [167, 86], [166, 99], [166, 107], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¤Ĩ, ë¡ij, ë¡ij, ï®ı, ðIJŃĬ, ðĿĶĵ, ðŁİij], [ï¦ĥ, ìŁĻ, ìŤ, à´´, íĵ¬, ðIJ±ħ, ðŁİij], [ï¦ĥ, áĨº, ï®ı, áīIJ, ðĿĺļ, ê²´, ðŁİij], [ï¦ĥ, ìĿ¿, ë¡ij, Éĺ, ðĿij³, áłĦ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_32><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_8><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[167, 81], [166, 89], [166, 99], [166, 107], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¦ĥ, ìŁĻ, ìŤ, à´´, íĵ¬, ðIJ±ħ, ðŁİij], [ï¦ĥ, áĨº, ï®ı, áīIJ, ðĿĺļ, ê²´, ðŁİij], [ï¦ĥ, ìĿ¿, ë¡ij, Éĺ, ðĿij³, áłĦ, ðŁİij], [ï¤ħ, 㳬, Ḱ, à´´, 䶵, á¸ĥ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[167, 86], [166, 93], [166, 103], [164, 111], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¦ĥ, áĨº, ï®ı, áīIJ, ðĿĺļ, ê²´, ðŁİij], [ï¦ĥ, ìĿ¿, ë¡ij, Éĺ, ðĿij³, áłĦ, ðŁİij], [ï¤ħ, 㳬, Ḱ, à´´, 䶵, á¸ĥ, ðŁİij], [ï¤Ĩ, ìī¡, âĦ¬, ðŁķĭ, áĮĦ, áĶħ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_75><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_75><DEPTH_101><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_69><DEPTH_56><DEPTH_32><DEPTH_8><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_69><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[166, 89], [166, 93], [166, 103], [164, 111], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¦ĥ, ìĿ¿, ë¡ij, Éĺ, ðĿij³, áłĦ, ðŁİij], [ï¤ħ, 㳬, Ḱ, à´´, 䶵, á¸ĥ, ðŁİij], [ï¤Ĩ, ìī¡, âĦ¬, ðŁķĭ, áĮĦ, áĶħ, ðŁİij], [ï¦ĥ, ìŤ, ðŁĸ¶, Ḱ, à¬ī, áī¨, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_75><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_69><DEPTH_56><DEPTH_32><DEPTH_8><DEPTH_26><DEPTH_26><DEPTH_32><DEPTH_56><DEPTH_69><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[166, 93], [166, 99], [166, 107], [164, 111], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¤ħ, 㳬, Ḱ, à´´, 䶵, á¸ĥ, ðŁİij], [ï¤Ĩ, ìī¡, âĦ¬, ðŁķĭ, áĮĦ, áĶħ, ðŁİij], [ï¦ĥ, ìŤ, ðŁĸ¶, Ḱ, à¬ī, áī¨, ðŁİij], [ï¤Ĩ, ìŁĻ, ê¸ij, áĮ½, Ôij, áĶĬ, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_26><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_26><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_8><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_69><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_56><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[166, 99], [166, 103], [166, 107], [164, 111], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¤Ĩ, ìī¡, âĦ¬, ðŁķĭ, áĮĦ, áĶħ, ðŁİij], [ï¦ĥ, ìŤ, ðŁĸ¶, Ḱ, à¬ī, áī¨, ðŁİij], [ï¤Ĩ, ìŁĻ, ê¸ij, áĮ½, Ôij, áĶĬ, ðŁİij], [ï¦ĥ, âļĤ, áĪ¾, á¿ļ, à¤ij, áī¨, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_26><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_26><DEPTH_26><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_69><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_8><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[166, 103], [166, 107], [164, 111], [164, 113], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¦ĥ, ìŤ, ðŁĸ¶, Ḱ, à¬ī, áī¨, ðŁİij], [ï¤Ĩ, ìŁĻ, ê¸ij, áĮ½, Ôij, áĶĬ, ðŁİij], [ï¦ĥ, âļĤ, áĪ¾, á¿ļ, à¤ij, áī¨, ðŁİij], [ï¨Ĥ, ï¨Ĥ, íģ», íģ», Ý¥, à°³, ðŁİij]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_69><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_69><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_75><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_69><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_55><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[166, 107], [164, 111], [164, 113], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¤Ĩ, ìŁĻ, ê¸ij, áĮ½, Ôij, áĶĬ, ðŁİij], [ï¦ĥ, âļĤ, áĪ¾, á¿ļ, à¤ij, áī¨, ðŁİij], [ï¨Ĥ, ï¨Ĥ, íģ», íģ», Ý¥, à°³, ðŁİij], [ï¨Ĥ, ðIJŃĵ, ì»ģ, â¡¢, ÔĪ, à¤ij, Ý¥]]." ] }
null
{ "from": [ "human", "gpt" ], "value": [ "The task is pick up the biscuit box and place it in the basket. What is the action that the robot should take. To figure out the action that the robot should take to pick up the biscuit box and place it in the basket, let's think through it step by step. First, what is the depth map for the first image? Second, how many subtasks are needed to complete this task, what is the semantic description of each subtask, and what are the goal positions for each subtask? Third, what is the trajectory of the end effector in the first image to reach the next subtask goal? Based on the depth map of the first image, the semantic description and goal position of each subtask, the trajectory of the end effector in the first image, along with other images from different camera views as additional information, what is the action that the robot should take?", "The depth map of the first image is <DEPTH_START><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_8><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_56><DEPTH_101><DEPTH_32><DEPTH_101><DEPTH_56><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_26><DEPTH_26><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_56><DEPTH_32><DEPTH_32><DEPTH_8><DEPTH_8><DEPTH_101><DEPTH_101><DEPTH_69><DEPTH_32><DEPTH_32><DEPTH_101><DEPTH_69><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_75><DEPTH_101><DEPTH_32><DEPTH_56><DEPTH_8><DEPTH_56><DEPTH_32><DEPTH_101><DEPTH_101><DEPTH_32><DEPTH_26><DEPTH_75><DEPTH_32><DEPTH_56><DEPTH_56><DEPTH_32><DEPTH_75><DEPTH_56><DEPTH_56><DEPTH_56><DEPTH_75><DEPTH_101><DEPTH_56><DEPTH_56><DEPTH_101><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_75><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_101><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_75><DEPTH_26><DEPTH_26><DEPTH_75><DEPTH_26><DEPTH_55><DEPTH_25><DEPTH_28><DEPTH_28><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_55><DEPTH_28><DEPTH_END>. The number of subtasks is 2. The subtask goals are: pick up the biscuit box at [164, 113] and place the biscuit box in the basket at [64, 63]. The trajectory of the end effector in the first image is [[164, 111], [164, 113], [164, 113]]. Based on these information, along with other images from different camera views as additional information, the action that the robot should take is [[ï¦ĥ, âļĤ, áĪ¾, á¿ļ, à¤ij, áī¨, ðŁİij], [ï¨Ĥ, ï¨Ĥ, íģ», íģ», Ý¥, à°³, ðŁİij], [ï¨Ĥ, ðIJŃĵ, ì»ģ, â¡¢, ÔĪ, à¤ij, Ý¥], [ï¨Ĥ, á¨ģ, ðĿĵ¹, íĵ¬, ðŁıĩ, à¬ī, Ý¥]]." ] }
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