kanishqgandharv/olsd-v0.1

--- license: apache-2.0 task_categories: - reinforcement-learning tags: - robotics - locomotion - mujoco - gymnasium - benchmark - offline-rl language: - en size_categories: - 1M<n<10M pretty_name: Open Locomotion Skills Dataset (OLSD) --- # Open Locomotion Skills Dataset (OLSD) v0.1 A unified benchmark dataset for legged robot locomotion across diverse morphologies. ## Dataset Description OLSD provides expert, random, and domain-randomized trajectories for training and evaluating locomotion policies. All data is collected from MuJoCo/Gymnasium environments. | Metric | Value | |--------|-------| | Total Episodes | ~4,200 | | Total Steps | ~2,722,712 | | Robots | HalfCheetah, Ant, Walker2d, Hopper | | Quality Tiers | random, expert, domain_random | | Format | Parquet + metadata JSON | ## Benchmark Results | Robot | Algorithm | Norm. Score | Return | |-------|-----------|-------------|--------| | Hopper | PPO | **108.4** | 3,507.9 | | Walker2d | SAC | **107.3** | 4,928.5 | | Ant | PPO | 29.7 | 1,733.2 | | HalfCheetah | PPO | 18.0 | 1,955.4 | ## Usage ```python from datasets import load_dataset # Load the full dataset ds = load_dataset("kanishqgandharv/olsd-v0.1") # Or load a specific robot ds = load_dataset("kanishqgandharv/olsd-v0.1", data_files="halfcheetah/**/*.parquet") ``` Or with the OLSD SDK: ```python import olsd dataset = olsd.load("./data/olsd-v0.1-final") print(dataset.summary()) ``` ## Data Schema Each row contains: - `observation.joint_positions` -- Joint angles (rad) - `observation.joint_velocities` -- Joint velocities (rad/s) - `action` -- Motor commands - `reward` -- Reward signal - `done` -- Terminal flag ## Links - [GitHub](https://github.com/KanishqGandharv219/open-locomotion-dataset) - [Website](https://kanishqgandharv219.github.io/open-locomotion-dataset/) - [Quickstart Notebook](https://github.com/KanishqGandharv219/open-locomotion-dataset/blob/main/notebooks/quickstart.ipynb) ## License Apache 2.0