ManiSkill: A Unified Benchmark for Generalizable Manipulation Skills

ManiSkill addresses the fragmentation in robotics benchmarks by providing a single, GPU-parallelized platform for generalizable manipulation. Built on SAPIEN (a high-fidelity physics engine with GPU-accelerated contact handling), ManiSkill supports 20+ tasks including articulated object manipulation (doors, drawers, faucets), tool use (hammering, sweeping), and deformable object handling (cloth folding). All tasks share a unified observation space (RGB-D + segmentation + point cloud) and action space (end-effector pose or joint velocity), making it easy to train and compare policies across tasks. The key technical contribution is GPU batching: ManiSkill runs thousands of parallel environments on a single GPU, achieving 1M+ environment steps per second on an RTX 3090. This is 100x faster than MuJoCo-based benchmarks and enables large-scale RL training (PPO, SAC) and imitation learning (ACT, Diffusion Policy) in hours instead of days. The platform also includes pre-generated demonstration datasets (100K+ trajectories per task) collected with motion planning, enabling offline IL training without real robots. ManiSkill 2.0 (2024) adds support for soft-body physics (cloth, rope, fluids), mobile manipulation (fetching with a mobile base), and humanoid whole-body control. The benchmark has been adopted by the community for evaluating VLA models: OpenVLA, π0, and Octo all report ManiSkill scores as standard metrics. The evaluation protocol includes generalization tests across object instances, poses, and lighting conditions, ensuring policies learn semantic skills rather than memorization. For practitioners, ManiSkill is the fastest way to prototype a manipulation policy: `pip install mani_skill`, load a task, and start training with Stable Baselines3 or your own IL pipeline. The visualizers (3D web viewer + recording) make debugging intuitive.