📊 We're releasing a massive dataset and generation tools to help the community solve these tasks

• 466GB of RGBD + state data
• 44K episodes
• 8.8M transitions
• Detailed event labeling + trajectory filtering

Download: arth-shukla.github.io/mshab/#dataset-section
(4/5)

🤖 We provide extensive RL & IL baselines and model checkpoints for whole-body control, tackling complex, very long-horizon rearrangement tasks. Each task chains multiple skills (Pick, Place, Open, Close) with simultaneous navigation & manipulation. (3/5)

⚡️ MS-HAB provides a GPU-accelerated implementation of the Home Assistant Benchmark (HAB) with realistic low-level control for successful grasping, manipulation, & interaction, all while achieving 3x the speed of prior work at similar GPU memory usage. (2/5)

📢 Introducing ManiSkill-HAB: A benchmark for low-level manipulation in home rearrangement tasks!

- GPU-accelerated simulation
- Extensive RL/IL baselines
- Vision-based, whole-body control robot dataset

All open-sourced: arth-shukla.github.io/mshab
🧵(1/5)