AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer

Abstract

Simulation parameter settings such as contact models and object geometry approximations are critical to training robust manipulation policies capable of transferring from simulation to real-world deployment. There is often an irreducible gap between simulation and reality: attempting to match the dynamics between simulation and reality may be infeasible and may not lead to policies that perform well in reality for a specific task. We propose AdaptSim, a new task-driven adaptation framework for sim-to-real transfer that aims to optimize task performance in target (real) environments. First, we meta-learn an adaptation policy in simulation using reinforcement learning for adjusting the simulation parameter distribution based on the current policy's performance in a target environment. We then perform iterative real-world adaptation by inferring new simulation parameter distributions for policy training. Our extensive simulation and hardware experiments demonstrate AdaptSim achieving 1-3x asymptotic performance and 2x real data efficiency when adapting to different environments, compared to methods based on Sys-ID and directly training the task policy in target environments.