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

Allen Z. Ren, Hongkai Dai, Benjamin Burchfiel, Anirudha Majumdar

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish (US)
JournalProceedings of Machine Learning Research
StatePublished - 2023
Event7th Conference on Robot Learning, CoRL 2023 - Atlanta, United States
Duration: Nov 6 2023Nov 9 2023

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


Dive into the research topics of 'AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer'. Together they form a unique fingerprint.

Cite this