DexSuite: An All-in-One Framework for Dexterous Manipulation Across Arms, Hands, and Tasks
Organizations: Mila · McGill University · Hugging Face · Kinova ·
Abstract
Human-level dexterity remains a central open problem in robotics, yet few simulation frameworks cover the full workflow of dexterous manipulation, from setting up a robot to collecting dexterous demonstrations and evaluating policies. We present DexSuite, an all-in-one simulation framework for dexterous manipulation built on the Genesis simulator. DexSuite provides 30 tasks and assembles its 10 arms and 12 end-effectors, from parallel-jaw grippers to dexterous hands, into more than 100 single-arm robots and thousands of bimanual pairs. It also ships a standardized environment API, integrated teleoperation, and simulated tactile sensing. Moreover, we define a benchmark of 16 task-embodiment pairs spanning diverse contact complexities and horizons, and release a dataset of 4,700 successful episodes in simulation from human teleoperation, scripted experts, and learned policies. We release DexSuite as open source and hope others will build on it. Videos and datasets are available at https://dexsuiteorg.github.io
Figures & tables
| Name | Physics | Parallel | Rigid Deform/Fluid | Robot Library | Dex Hands | Bimanual | Embodiment Independent | Hand Dataset | Hand Teleop | Tactile Sensing |
| DexSuite | Genesis | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| robosuite | MuJoCo | ✗ | ✗ | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ | ✗ |
| Isaac Lab | PhysX | ✓ | ✗ | ✓ | ✓ | ✓ | ✗ | ✗ | ✓ | ✓ |
| RLBench | CoppeliaSim | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| ManiSkill3 | SAPIEN/PhysX | ✓ | ✗ | ✓ | ✓ | ✓ | ✗ | ✗ | ✗ | ✓ |
| OmniGibson | PhysX | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ | ✓ | ✗ |
| Track 1 | Track 2 | |||||||
| Parallel-Jaw Gripper | Allegro Hand | |||||||
| Model / Input | Reach | Lift | Push | Pick & Place | Stack | Rack Mug | Move Pan | Drill to Point |
| BC-G (state) | 78{\color[rgb]{0.5,0.5,0.5}\,\pm\,5} | 31{\color[rgb]{0.5,0.5,0.5}\,\pm\,5} | 27{\color[rgb]{0.5,0.5,0.5}\,\pm\,6} | 49{\color[rgb]{0.5,0.5,0.5}\,\pm\,14} | 0{\color[rgb]{0.5,0.5,0.5}\,\pm\,0} | 25{\color[rgb]{0.5,0.5,0.5}\,\pm\,8} | 5{\color[rgb]{0.5,0.5,0.5}\,\pm\,5} | 1{\color[rgb]{0.5,0.5,0.5}\,\pm\,1} |
| BC-GMM (state) | 76{\color[rgb]{0.5,0.5,0.5}\,\pm\,2} | 32{\color[rgb]{0.5,0.5,0.5}\,\pm\,5} | 23{\color[rgb]{0.5,0.5,0.5}\,\pm\,6} | 24{\color[rgb]{0.5,0.5,0.5}\,\pm\,6} | 0{\color[rgb]{0.5,0.5,0.5}\,\pm\,0} | 28{\color[rgb]{0.5,0.5,0.5}\,\pm\,2} | 13{\color[rgb]{0.5,0.5,0.5}\,\pm\,6} | 5{\color[rgb]{0.5,0.5,0.5}\,\pm\,2} |
| BC-T (state) | 90{\color[rgb]{0.5,0.5,0.5}\,\pm\,2} | 57{\color[rgb]{0.5,0.5,0.5}\,\pm\,8} | 48{\color[rgb]{0.5,0.5,0.5}\,\pm\,4} | 64{\color[rgb]{0.5,0.5,0.5}\,\pm\,0} | 2{\color[rgb]{0.5,0.5,0.5}\,\pm\,0} | 58{\color[rgb]{0.5,0.5,0.5}\,\pm\,3} | 68{\color[rgb]{0.5,0.5,0.5}\,\pm\,16} | 47{\color[rgb]{0.5,0.5,0.5}\,\pm\,8} |
| Diffusion (state) | \bm{100}{\color[rgb]{0.5,0.5,0.5}\,\pm\,0} | \bm{72}{\color[rgb]{0.5,0.5,0.5}\,\pm\,3} | \bm{63}{\color[rgb]{0.5,0.5,0.5}\,\pm\,2} | \bm{65}{\color[rgb]{0.5,0.5,0.5}\,\pm\,5} | \bm{16}{\color[rgb]{0.5,0.5,0.5}\,\pm\,2} | \bm{82}{\color[rgb]{0.5,0.5,0.5}\,\pm\,2} | \bm{69}{\color[rgb]{0.5,0.5,0.5}\,\pm\,6} | \bm{50}{\color[rgb]{0.5,0.5,0.5}\,\pm\,2} |
Robotic Manipulation
Robotic manipulation studies how robots perceive, plan, and control physical interactions to grasp, move, assemble, and use objects.
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