5 citations · 7 across the 5 of their papers we have counts for
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cs.RO2022★ 1 cited
Global Planning for Contact-Rich Manipulation via Local Smoothing of Quasi-dynamic Contact Models
Tao Pang, H. J. Terry Suh, Lujie Yang +1
The empirical success of Reinforcement Learning (RL) in the setting of contact-rich manipulation leaves much to be understood from a model-based perspective, where the key difficul…
cs.LG2022★ 5 cited
Do Differentiable Simulators Give Better Policy Gradients?
H. J. Terry Suh, Max Simchowitz, Kaiqing Zhang +1
Differentiable simulators promise faster computation time for reinforcement learning by replacing zeroth-order gradient estimates of a stochastic objective with an estimate based o…