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ManiSkill is a benchmark platform for training and evaluating reinforcement learning agents on dexterous manipulation tasks using physics-based simulations. Developed by Hao Su Lab, it focuses on robotic manipulation with diverse, high-quality 3D tasks designed to challenge perception, control, and planning in robotics. ManiSkill provides both low-level control and visual observation spaces for realistic learning scenarios.
Features
- Includes a diverse set of dexterous manipulation tasks and scenarios
- Offers both low-dimensional state and high-dimensional visual observations
- Supports physics-based simulation using the Isaac Gym engine
- Compatible with common RL algorithms and libraries like Stable-Baselines3
- Provides standardized benchmarks for robotic manipulation research
- Facilitates multi-task learning and generalization across tasks
Project Samples
Categories
Reinforcement Learning FrameworksLicense
Apache License V2.0Follow ManiSkill
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