# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from abc import ABC, abstractmethod from gr00t.data.dataset import ModalityConfig from gr00t.data.transform.base import ComposedModalityTransform, ModalityTransform from gr00t.data.transform.concat import ConcatTransform from gr00t.data.transform.state_action import StateActionToTensor, StateActionTransform from gr00t.data.transform.video import ( VideoColorJitter, VideoCrop, VideoResize, VideoToNumpy, VideoToTensor, ) from gr00t.model.transforms import GR00TTransform class BaseDataConfig(ABC): @abstractmethod def modality_config(self) -> dict[str, ModalityConfig]: pass @abstractmethod def transform(self) -> ModalityTransform: pass ########################################################################################### class Gr1ArmsOnlyDataConfig(BaseDataConfig): video_keys = ["video.ego_view"] state_keys = [ "state.left_arm", "state.right_arm", "state.left_hand", "state.right_hand", ] action_keys = [ "action.left_arm", "action.right_arm", "action.left_hand", "action.right_hand", ] language_keys = ["annotation.human.action.task_description"] observation_indices = [0] action_indices = list(range(16)) def modality_config(self) -> dict[str, ModalityConfig]: video_modality = ModalityConfig( delta_indices=self.observation_indices, modality_keys=self.video_keys, ) state_modality = ModalityConfig( delta_indices=self.observation_indices, modality_keys=self.state_keys, ) action_modality = ModalityConfig( delta_indices=self.action_indices, modality_keys=self.action_keys, ) language_modality = ModalityConfig( delta_indices=self.observation_indices, modality_keys=self.language_keys, ) modality_configs = { "video": video_modality, "state": state_modality, "action": action_modality, "language": language_modality, } return modality_configs def transform(self) -> ModalityTransform: transforms = [ # video transforms VideoToTensor(apply_to=self.video_keys), VideoCrop(apply_to=self.video_keys, scale=0.95), VideoResize(apply_to=self.video_keys, height=224, width=224, interpolation="linear"), VideoColorJitter( apply_to=self.video_keys, brightness=0.3, contrast=0.4, saturation=0.5, hue=0.08, ), VideoToNumpy(apply_to=self.video_keys), # state transforms StateActionToTensor(apply_to=self.state_keys), StateActionTransform( apply_to=self.state_keys, normalization_modes={key: "min_max" for key in self.state_keys}, ), # action transforms StateActionToTensor(apply_to=self.action_keys), StateActionTransform( apply_to=self.action_keys, normalization_modes={key: "min_max" for key in self.action_keys}, ), # concat transforms ConcatTransform( video_concat_order=self.video_keys, state_concat_order=self.state_keys, action_concat_order=self.action_keys, ), # model-specific transform GR00TTransform( state_horizon=len(self.observation_indices), action_horizon=len(self.action_indices), max_state_dim=64, max_action_dim=32, ), ] return ComposedModalityTransform(transforms=transforms) ########################################################################################### class So100DataConfig(BaseDataConfig): video_keys = ["video.webcam"] state_keys = ["state.single_arm", "state.gripper"] action_keys = ["action.single_arm", "action.gripper"] language_keys = ["annotation.human.task_description"] observation_indices = [0] action_indices = list(range(16)) def modality_config(self) -> dict[str, ModalityConfig]: video_modality = ModalityConfig( delta_indices=self.observation_indices, modality_keys=self.video_keys, ) state_modality = ModalityConfig( delta_indices=self.observation_indices, modality_keys=self.state_keys, ) action_modality = ModalityConfig( delta_indices=self.action_indices, modality_keys=self.action_keys, ) language_modality = ModalityConfig( delta_indices=self.observation_indices, modality_keys=self.language_keys, ) modality_configs = { "video": video_modality, "state": state_modality, "action": action_modality, "language": language_modality, } return modality_configs def transform(self) -> ModalityTransform: transforms = [ # video transforms VideoToTensor(apply_to=self.video_keys), VideoCrop(apply_to=self.video_keys, scale=0.95), VideoResize(apply_to=self.video_keys, height=224, width=224, interpolation="linear"), VideoColorJitter( apply_to=self.video_keys, brightness=0.3, contrast=0.4, saturation=0.5, hue=0.08, ), VideoToNumpy(apply_to=self.video_keys), # state transforms StateActionToTensor(apply_to=self.state_keys), StateActionTransform( apply_to=self.state_keys, normalization_modes={key: "min_max" for key in self.state_keys}, ), # action transforms StateActionToTensor(apply_to=self.action_keys), StateActionTransform( apply_to=self.action_keys, normalization_modes={key: "min_max" for key in self.action_keys}, ), # concat transforms ConcatTransform( video_concat_order=self.video_keys, state_concat_order=self.state_keys, action_concat_order=self.action_keys, ), # model-specific transform GR00TTransform( state_horizon=len(self.observation_indices), action_horizon=len(self.action_indices), max_state_dim=64, max_action_dim=32, ), ] return ComposedModalityTransform(transforms=transforms) ########################################################################################### class Gr1FullUpperBodyDataConfig(BaseDataConfig): video_keys = ["video.front_view"] state_keys = [ "state.left_arm", "state.right_arm", "state.left_hand", "state.right_hand", "state.waist", "state.neck", ] action_keys = [ "action.left_arm", "action.right_arm", "action.left_hand", "action.right_hand", "action.waist", "action.neck", ] language_keys = ["annotation.human.action.task_description"] observation_indices = [0] action_indices = list(range(16)) def modality_config(self): video_modality = ModalityConfig( delta_indices=self.observation_indices, modality_keys=self.video_keys, ) state_modality = ModalityConfig( delta_indices=self.observation_indices, modality_keys=self.state_keys, ) action_modality = ModalityConfig( delta_indices=self.action_indices, modality_keys=self.action_keys, ) language_modality = ModalityConfig( delta_indices=self.observation_indices, modality_keys=self.language_keys, ) modality_configs = { "video": video_modality, "state": state_modality, "action": action_modality, "language": language_modality, } return modality_configs def transform(self): transforms = [ # video transforms VideoToTensor(apply_to=self.video_keys), VideoCrop(apply_to=self.video_keys, scale=0.95), VideoResize(apply_to=self.video_keys, height=224, width=224, interpolation="linear"), VideoColorJitter( apply_to=self.video_keys, brightness=0.3, contrast=0.4, saturation=0.5, hue=0.08, ), VideoToNumpy(apply_to=self.video_keys), # state transforms StateActionToTensor(apply_to=self.state_keys), StateActionTransform( apply_to=self.state_keys, normalization_modes={ "state.left_arm": "min_max", "state.right_arm": "min_max", "state.left_hand": "min_max", "state.right_hand": "min_max", "state.waist": "min_max", "state.neck": "min_max", }, ), # action transforms StateActionToTensor(apply_to=self.action_keys), StateActionTransform( apply_to=self.action_keys, normalization_modes={ "action.right_arm": "min_max", "action.left_arm": "min_max", "action.right_hand": "min_max", "action.left_hand": "min_max", "action.waist": "min_max", "action.neck": "min_max", }, ), # concat transforms ConcatTransform( video_concat_order=self.video_keys, state_concat_order=self.state_keys, action_concat_order=self.action_keys, ), GR00TTransform( state_horizon=len(self.observation_indices), action_horizon=len(self.action_indices), max_state_dim=64, max_action_dim=32, ), ] return ComposedModalityTransform(transforms=transforms) ########################################################################################### class BimanualPandaGripperDataConfig(BaseDataConfig): video_keys = [ "video.right_wrist_view", "video.left_wrist_view", "video.front_view", ] state_keys = [ "state.right_arm_eef_pos", "state.right_arm_eef_quat", "state.right_gripper_qpos", "state.left_arm_eef_pos", "state.left_arm_eef_quat", "state.left_gripper_qpos", ] action_keys = [ "action.right_arm_eef_pos", "action.right_arm_eef_rot", "action.right_gripper_close", "action.left_arm_eef_pos", "action.left_arm_eef_rot", "action.left_gripper_close", ] language_keys = ["annotation.human.action.task_description"] observation_indices = [0] action_indices = list(range(16)) def modality_config(self): video_modality = ModalityConfig( delta_indices=self.observation_indices, modality_keys=self.video_keys, ) state_modality = ModalityConfig( delta_indices=self.observation_indices, modality_keys=self.state_keys, ) action_modality = ModalityConfig( delta_indices=self.action_indices, modality_keys=self.action_keys, ) language_modality = ModalityConfig( delta_indices=self.observation_indices, modality_keys=self.language_keys, ) modality_configs = { "video": video_modality, "state": state_modality, "action": action_modality, "language": language_modality, } return modality_configs def transform(self): transforms = [ # video transforms VideoToTensor(apply_to=self.video_keys), VideoCrop(apply_to=self.video_keys, scale=0.95), VideoResize(apply_to=self.video_keys, height=224, width=224, interpolation="linear"), VideoColorJitter( apply_to=self.video_keys, brightness=0.3, contrast=0.4, saturation=0.5, hue=0.08, ), VideoToNumpy(apply_to=self.video_keys), # state transforms StateActionToTensor(apply_to=self.state_keys), StateActionTransform( apply_to=self.state_keys, normalization_modes={ "state.right_arm_eef_pos": "min_max", "state.right_gripper_qpos": "min_max", "state.left_arm_eef_pos": "min_max", "state.left_gripper_qpos": "min_max", }, target_rotations={ "state.right_arm_eef_quat": "rotation_6d", "state.left_arm_eef_quat": "rotation_6d", }, ), # action transforms StateActionToTensor(apply_to=self.action_keys), StateActionTransform( apply_to=self.action_keys, normalization_modes={ "action.right_gripper_close": "binary", "action.left_gripper_close": "binary", }, ), # concat transforms ConcatTransform( video_concat_order=self.video_keys, state_concat_order=self.state_keys, action_concat_order=self.action_keys, ), GR00TTransform( state_horizon=len(self.observation_indices), action_horizon=len(self.action_indices), max_state_dim=64, max_action_dim=32, ), ] return ComposedModalityTransform(transforms=transforms) ########################################################################################### class BimanualPandaHandDataConfig(BaseDataConfig): video_keys = [ "video.right_wrist_view", "video.left_wrist_view", "video.ego_view", ] state_keys = [ "state.right_arm_eef_pos", "state.right_arm_eef_quat", "state.right_hand", "state.left_arm_eef_pos", "state.left_arm_eef_quat", "state.left_hand", ] action_keys = [ "action.right_arm_eef_pos", "action.right_arm_eef_rot", "action.right_hand", "action.left_arm_eef_pos", "action.left_arm_eef_rot", "action.left_hand", ] language_keys = ["annotation.human.action.task_description"] observation_indices = [0] action_indices = list(range(16)) def modality_config(self): video_modality = ModalityConfig( delta_indices=self.observation_indices, modality_keys=self.video_keys, ) state_modality = ModalityConfig( delta_indices=self.observation_indices, modality_keys=self.state_keys, ) action_modality = ModalityConfig( delta_indices=self.action_indices, modality_keys=self.action_keys, ) language_modality = ModalityConfig( delta_indices=self.observation_indices, modality_keys=self.language_keys, ) modality_configs = { "video": video_modality, "state": state_modality, "action": action_modality, "language": language_modality, } return modality_configs def transform(self): transforms = [ # video transforms VideoToTensor(apply_to=self.video_keys), VideoCrop(apply_to=self.video_keys, scale=0.95), VideoResize(apply_to=self.video_keys, height=224, width=224, interpolation="linear"), VideoColorJitter( apply_to=self.video_keys, brightness=0.3, contrast=0.4, saturation=0.5, hue=0.08, ), VideoToNumpy(apply_to=self.video_keys), # state transforms StateActionToTensor(apply_to=self.state_keys), StateActionTransform( apply_to=self.state_keys, normalization_modes={ "state.right_arm_eef_pos": "min_max", "state.right_hand": "min_max", "state.left_arm_eef_pos": "min_max", "state.left_hand": "min_max", }, target_rotations={ "state.right_arm_eef_quat": "rotation_6d", "state.left_arm_eef_quat": "rotation_6d", }, ), # action transforms StateActionToTensor(apply_to=self.action_keys), StateActionTransform( apply_to=self.action_keys, normalization_modes={ "action.right_hand": "min_max", "action.left_hand": "min_max", }, ), # concat transforms ConcatTransform( video_concat_order=self.video_keys, state_concat_order=self.state_keys, action_concat_order=self.action_keys, ), GR00TTransform( state_horizon=len(self.observation_indices), action_horizon=len(self.action_indices), max_state_dim=64, max_action_dim=32, ), ] return ComposedModalityTransform(transforms=transforms) ########################################################################################### class SinglePandaGripperDataConfig(BaseDataConfig): video_keys = [ "video.left_view", "video.right_view", "video.wrist_view", ] state_keys = [ "state.end_effector_position_relative", "state.end_effector_rotation_relative", "state.gripper_qpos", "state.base_position", "state.base_rotation", ] action_keys = [ "action.end_effector_position", "action.end_effector_rotation", "action.gripper_close", "action.base_motion", "action.control_mode", ] language_keys = ["annotation.human.action.task_description"] observation_indices = [0] action_indices = list(range(16)) def modality_config(self): video_modality = ModalityConfig( delta_indices=self.observation_indices, modality_keys=self.video_keys, ) state_modality = ModalityConfig( delta_indices=self.observation_indices, modality_keys=self.state_keys, ) action_modality = ModalityConfig( delta_indices=self.action_indices, modality_keys=self.action_keys, ) language_modality = ModalityConfig( delta_indices=self.observation_indices, modality_keys=self.language_keys, ) modality_configs = { "video": video_modality, "state": state_modality, "action": action_modality, "language": language_modality, } return modality_configs def transform(self): transforms = [ # video transforms VideoToTensor(apply_to=self.video_keys), VideoCrop(apply_to=self.video_keys, scale=0.95), VideoResize(apply_to=self.video_keys, height=224, width=224, interpolation="linear"), VideoColorJitter( apply_to=self.video_keys, brightness=0.3, contrast=0.4, saturation=0.5, hue=0.08, ), VideoToNumpy(apply_to=self.video_keys), # state transforms StateActionToTensor(apply_to=self.state_keys), StateActionTransform( apply_to=self.state_keys, normalization_modes={ "state.end_effector_position_relative": "min_max", "state.end_effector_rotation_relative": "min_max", "state.gripper_qpos": "min_max", "state.base_position": "min_max", "state.base_rotation": "min_max", }, target_rotations={ "state.end_effector_rotation_relative": "rotation_6d", "state.base_rotation": "rotation_6d", }, ), # action transforms StateActionToTensor(apply_to=self.action_keys), StateActionTransform( apply_to=self.action_keys, normalization_modes={ "action.end_effector_position": "min_max", "action.end_effector_rotation": "min_max", "action.gripper_close": "binary", "action.base_motion": "min_max", "action.control_mode": "binary", }, ), # concat transforms ConcatTransform( video_concat_order=self.video_keys, state_concat_order=self.state_keys, action_concat_order=self.action_keys, ), GR00TTransform( state_horizon=len(self.observation_indices), action_horizon=len(self.action_indices), max_state_dim=64, max_action_dim=32, ), ] return ComposedModalityTransform(transforms=transforms) ########################################################################################### class Gr1ArmsWaistDataConfig(Gr1ArmsOnlyDataConfig): video_keys = ["video.ego_view"] state_keys = [ "state.left_arm", "state.right_arm", "state.left_hand", "state.right_hand", "state.waist", ] action_keys = [ "action.left_arm", "action.right_arm", "action.left_hand", "action.right_hand", "action.waist", ] language_keys = ["annotation.human.coarse_action"] observation_indices = [0] action_indices = list(range(16)) def modality_config(self): return super().modality_config() def transform(self): return super().transform() ########################################################################################### class LekiwiDataConfig(BaseDataConfig): video_keys = ["video.wrist", "video.front"] state_keys = ["state.shoulder_pan", "state.shoulder_lift", "state.elbow_flex", "state.wrist_flex", "state.wrist_roll", "state.gripper", "state.left_wheel", "state.back_wheel", "state.right_wheel"] action_keys = ["action.shoulder_pan", "action.shoulder_lift", "action.elbow_flex", "action.wrist_flex", "action.wrist_roll", "action.gripper", "action.left_wheel", "action.back_wheel", "action.right_wheel"] language_keys = ["annotation.human.task_description"] observation_indices = [0] action_indices = list(range(16)) def modality_config(self) -> dict[str, ModalityConfig]: video_modality = ModalityConfig( delta_indices=self.observation_indices, modality_keys=self.video_keys, ) state_modality = ModalityConfig( delta_indices=self.observation_indices, modality_keys=self.state_keys, ) action_modality = ModalityConfig( delta_indices=self.action_indices, modality_keys=self.action_keys, ) language_modality = ModalityConfig( delta_indices=self.observation_indices, modality_keys=self.language_keys, ) modality_configs = { "video": video_modality, "state": state_modality, "action": action_modality, "language": language_modality, } return modality_configs def transform(self) -> ModalityTransform: transforms = [ # video transforms VideoToTensor(apply_to=self.video_keys), VideoCrop(apply_to=self.video_keys, scale=0.95), VideoResize(apply_to=self.video_keys, height=224, width=224, interpolation="linear"), VideoColorJitter( apply_to=self.video_keys, brightness=0.3, contrast=0.4, saturation=0.5, hue=0.08, ), VideoToNumpy(apply_to=self.video_keys), # state transforms StateActionToTensor(apply_to=self.state_keys), StateActionTransform( apply_to=self.state_keys, normalization_modes={key: "min_max" for key in self.state_keys}, ), # action transforms StateActionToTensor(apply_to=self.action_keys), StateActionTransform( apply_to=self.action_keys, normalization_modes={key: "min_max" for key in self.action_keys}, ), # concat transforms ConcatTransform( video_concat_order=self.video_keys, state_concat_order=self.state_keys, action_concat_order=self.action_keys, ), # model-specific transform GR00TTransform( state_horizon=len(self.observation_indices), action_horizon=len(self.action_indices), max_state_dim=64, max_action_dim=32, ), ] return ComposedModalityTransform(transforms=transforms) DATA_CONFIG_MAP = { "gr1_arms_waist": Gr1ArmsWaistDataConfig(), "gr1_arms_only": Gr1ArmsOnlyDataConfig(), "gr1_full_upper_body": Gr1FullUpperBodyDataConfig(), "bimanual_panda_gripper": BimanualPandaGripperDataConfig(), "bimanual_panda_hand": BimanualPandaHandDataConfig(), "single_panda_gripper": SinglePandaGripperDataConfig(), "so100": So100DataConfig(), "lekiwi": LekiwiDataConfig() }