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dataset_info.json

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+{
+  "citation": "@inproceedings{vogel_edan_2020,\n\ttitle = {EDAN - an EMG-Controlled Daily Assistant to Help People with Physical Disabilities},\n\tlanguage = {en},\n\tbooktitle = {2020 {IEEE}/{RSJ} {International} {Conference} on {Intelligent} {Robots} and {Systems} ({IROS})},\n\tauthor = {Vogel, J\u00f6rn and Hagengruber, Annette and Iskandar, Maged and Quere, Gabriel and Leipscher, Ulrike and Bustamante, Samuel and Dietrich, Alexander and Hoeppner, Hannes and Leidner, Daniel and Albu-Sch\u00e4ffer, Alin},\n\tyear = {2020}\n}\n@inproceedings{quere_shared_2020,\n\taddress = {Paris, France},\n\ttitle = {Shared {Control} {Templates} for {Assistive} {Robotics}},\n\tlanguage = {en},\n\tbooktitle = {2020 {IEEE} {International} {Conference} on {Robotics} and {Automation} ({ICRA})},\n\tauthor = {Quere, Gabriel and Hagengruber, Annette and Iskandar, Maged and Bustamante, Samuel and Leidner, Daniel and Stulp, Freek and Vogel, Joern},\n\tyear = {2020},\n\tpages = {7},\n}",
+  "description": "wheelchair with arm performing shelf pick tasks",
+  "fileFormat": "tfrecord",
+  "location": {
+    "urls": [
+      "https://ieeexplore.ieee.org/document/9341156"
+    ]
+  },
+  "moduleName": "tensorflow_datasets.robotics.rtx.rtx",
+  "name": "dlr_edan_shared_control_converted_externally_to_rlds",
+  "releaseNotes": {
+    "0.1.0": "Initial release."
+  },
+  "splits": [
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+      "name": "train",
+      "numBytes": "3316378421",
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features.json

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+{
+    "featuresDict": {
+        "features": {
+            "episode_metadata": {
+                "featuresDict": {
+                    "features": {
+                        "file_path": {
+                            "description": "Path to the original data file.",
+                            "pythonClassName": "tensorflow_datasets.core.features.text_feature.Text",
+                            "text": {}
+                        }
+                    }
+                },
+                "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict"
+            },
+            "steps": {
+                "pythonClassName": "tensorflow_datasets.core.features.dataset_feature.Dataset",
+                "sequence": {
+                    "feature": {
+                        "featuresDict": {
+                            "features": {
+                                "action": {
+                                    "description": "Robot action, consists of [3x robot EEF position, 3x robot EEF orientation yaw/pitch/roll calculated with scipy Rotation.as_euler(=\"zxy\") Class].",
+                                    "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor",
+                                    "tensor": {
+                                        "dtype": "float32",
+                                        "encoding": "none",
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+                                            ]
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+                                "discount": {
+                                    "description": "Discount if provided, default to 1.",
+                                    "pythonClassName": "tensorflow_datasets.core.features.scalar.Scalar",
+                                    "tensor": {
+                                        "dtype": "float32",
+                                        "encoding": "none",
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+                                "language_embedding": {
+                                    "description": "Kona language embedding. See https://tfhub.dev/google/universal-sentence-encoder-large/5",
+                                    "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor",
+                                    "tensor": {
+                                        "dtype": "float32",
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+                                        "shape": {
+                                            "dimensions": [
+                                                "512"
+                                            ]
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+                                "language_instruction": {
+                                    "description": "Pour into the mug.",
+                                    "pythonClassName": "tensorflow_datasets.core.features.text_feature.Text",
+                                    "text": {}
+                                },
+                                "observation": {
+                                    "featuresDict": {
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+                                            "image": {
+                                                "description": "Main camera RGB observation.",
+                                                "image": {
+                                                    "dtype": "uint8",
+                                                    "encodingFormat": "png",
+                                                    "shape": {
+                                                        "dimensions": [
+                                                            "360",
+                                                            "640",
+                                                            "3"
+                                                        ]
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+                                                "pythonClassName": "tensorflow_datasets.core.features.image_feature.Image"
+                                            },
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+                                                "description": "Robot state, consists of [3x robot EEF position, 3x robot EEF orientation yaw/pitch/roll calculated with scipy Rotation.as_euler(=\"zxy\") Class].",
+                                                "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor",
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+                                    "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict"
+                                },
+                                "reward": {
+                                    "description": "Reward if provided, 1 on final step for demos.",
+                                    "pythonClassName": "tensorflow_datasets.core.features.scalar.Scalar",
+                                    "tensor": {
+                                        "dtype": "float32",
+                                        "encoding": "none",
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+}