QuasiSim: Parameterized Quasi-Physical Simulators for Dexterous Manipulations Transfer
This addresses the challenge of transferring human manipulations to dexterous robot hands, which is incremental as it builds on prior simulation-based methods.
The paper tackles the dexterous manipulation transfer problem by designing parameterized quasi-physical simulators and a physics curriculum to balance fidelity and optimizability, resulting in an 11%+ boost in success rate for tracking complex manipulations in high-fidelity simulated environments.
We explore the dexterous manipulation transfer problem by designing simulators. The task wishes to transfer human manipulations to dexterous robot hand simulations and is inherently difficult due to its intricate, highly-constrained, and discontinuous dynamics and the need to control a dexterous hand with a DoF to accurately replicate human manipulations. Previous approaches that optimize in high-fidelity black-box simulators or a modified one with relaxed constraints only demonstrate limited capabilities or are restricted by insufficient simulation fidelity. We introduce parameterized quasi-physical simulators and a physics curriculum to overcome these limitations. The key ideas are 1) balancing between fidelity and optimizability of the simulation via a curriculum of parameterized simulators, and 2) solving the problem in each of the simulators from the curriculum, with properties ranging from high task optimizability to high fidelity. We successfully enable a dexterous hand to track complex and diverse manipulations in high-fidelity simulated environments, boosting the success rate by 11\%+ from the best-performed baseline. The project website is available at https://meowuu7.github.io/QuasiSim/.