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lerobot/asu_table_top

This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v2.0", "robot_type": "unknown", "total_episodes": 110, "total_frames": 26113, "total_tasks": 216, "total_videos": 110, "total_chunks": 1, "chunks_size": 1000, "fps": 5, "splits": { "train": "0:110" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path":… See the full description on the dataset page: https://huggingface.co/datasets/lerobot/asu_table_top.

sourceHugging Facemitupdated 1y agoView on Hugging Face
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Dataset Card

This dataset was created using LeRobot.

Dataset Description

  • —Homepage: [More Information Needed]
  • —Paper: https://link.springer.com/article/10.1007/s10514-023-10129-1
  • —License: mit

Dataset Structure

meta/info.json:

json
{
    "codebase_version": "v2.0",
    "robot_type": "unknown",
    "total_episodes": 110,
    "total_frames": 26113,
    "total_tasks": 216,
    "total_videos": 110,
    "total_chunks": 1,
    "chunks_size": 1000,
    "fps": 5,
    "splits": {
        "train": "0:110"
    },
    "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
    "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
    "features": {
        "observation.images.image": {
            "dtype": "video",
            "shape": [
                224,
                224,
                3
            ],
            "names": [
                "height",
                "width",
                "channel"
            ],
            "video_info": {
                "video.fps": 5.0,
                "video.codec": "av1",
                "video.pix_fmt": "yuv420p",
                "video.is_depth_map": false,
                "has_audio": false
            }
        },
        "language_instruction": {
            "dtype": "string",
            "shape": [
                1
            ],
            "names": null
        },
        "observation.state": {
            "dtype": "float32",
            "shape": [
                7
            ],
            "names": {
                "motors": [
                    "motor_0",
                    "motor_1",
                    "motor_2",
                    "motor_3",
                    "motor_4",
                    "motor_5",
                    "motor_6"
                ]
            }
        },
        "action": {
            "dtype": "float32",
            "shape": [
                7
            ],
            "names": {
                "motors": [
                    "motor_0",
                    "motor_1",
                    "motor_2",
                    "motor_3",
                    "motor_4",
                    "motor_5",
                    "motor_6"
                ]
            }
        },
        "timestamp": {
            "dtype": "float32",
            "shape": [
                1
            ],
            "names": null
        },
        "episode_index": {
            "dtype": "int64",
            "shape": [
                1
            ],
            "names": null
        },
        "frame_index": {
            "dtype": "int64",
            "shape": [
                1
            ],
            "names": null
        },
        "next.reward": {
            "dtype": "float32",
            "shape": [
                1
            ],
            "names": null
        },
        "next.done": {
            "dtype": "bool",
            "shape": [
                1
            ],
            "names": null
        },
        "index": {
            "dtype": "int64",
            "shape": [
                1
            ],
            "names": null
        },
        "task_index": {
            "dtype": "int64",
            "shape": [
                1
            ],
            "names": null
        }
    }
}

Citation

BibTeX:

bibtex
@inproceedings{zhou2023modularity,
    title={Modularity through Attention: Efficient Training and Transfer of Language-Conditioned Policies for Robot Manipulation},
    author={Zhou, Yifan and Sonawani, Shubham and Phielipp, Mariano and Stepputtis, Simon and Amor, Heni},
    booktitle={Conference on Robot Learning},
    pages={1684--1695},
    year={2023},
    organization={PMLR}
}
@article{zhou2023learning,
    title={Learning modular language-conditioned robot policies through attention},
    author={Zhou, Yifan and Sonawani, Shubham and Phielipp, Mariano and Ben Amor, Heni and Stepputtis, Simon},
    journal={Autonomous Robots},
    pages={1--21},
    year={2023},
    publisher={Springer}
}