  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/.
