QuasiSim: Parameterized Quasi-Physical Simulators for Dexterous
  Manipulations Transfer
Xueyi Liu Kangbo Lyu Jieqiong Zhang Tao Du Li Yi
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/.