QuasiSim: Parameterized Quasi-Physical Simulators for Dexterous Manipulations Transfer
<|reference_start|>QuasiSim: Parameterized Quasi-Physical Simulators for Dexterous Manipulations Transfer: 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/.<|reference_end|>
@article{liu2024quasisim:,
    title={QuasiSim: Parameterized Quasi-Physical Simulators for Dexterous
  Manipulations Transfer},
    author={Xueyi Liu, Kangbo Lyu, Jieqiong Zhang, Tao Du, Li Yi},
    journal={arXiv preprint arXiv:2404.07988},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07988},
    primaryClass={cs.RO cs.CV cs.GR}
}