  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 general geometric
representations, such as convex decomposition, triangular mesh, signed distance
field can improve simulation fidelity, and thus potentially narrow the reality
gap. Common to these approaches is that many contact points are generated for
geometrically-complex objects, which slows down simulation and may cause
numerical instability. Contact reduction methods address these issues by
limiting the number of contact points, but the validity of these methods for
sim-to-real RL has not been confirmed. In this paper, we present a contact
reduction method with bounded stiffness to improve the simulation accuracy. Our
experiments show that the proposed method critically enables training RL policy
for a tight-clearance double pin insertion task and successfully deploying the
policy on a rigid, position-controlled physical robot.
