Contact Reduction with Bounded Stiffness for Robust Sim-to-Real Transfer of Robot Assembly
Nghia Vuong,Quang-Cuong Pham,Nghia Vuong,Quang-Cuong Pham
In sim-to-real Reinforcement Learning (RL), a policy is trained in a simulated environment and then deployed on the physical system. The main challenge of sim-to-real RL is to overcome the reality gap - the discrepancies between the real world and its simulated counterpart. Using generic geometric representations, such as convex decomposition, triangular mesh, signed distance field can improve sim...
