TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction and Learning Base Policies

Table of Links

Abstract and 1 Introduction

2 Preliminaries

3 TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction and 3.1 Learning Base Policies in Simulation with RL

3.2 Learning Residual Policies from Online Correction

3.3 An Integrated Deployment Framework and 3.4 Implementation Details

4 Experiments

4.1 Experiment Settings

4.2 Quantitative Comparison on Four Assembly Tasks

4.3 Effectiveness in Addressing Different Sim-to-Real Gaps (Q4)

4.4 Scalability with Human Effort (Q5) and 4.5 Intriguing Properties and Emergent Behaviors (Q6)

5 Related Work

6 Conclusion and Limitations, Acknowledgments, and References

A. Simulation Training Details

B. Real-World Learning Details

C. Experiment Settings and Evaluation Details

D. Additional Experiment Results

3 TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correctio

An overview of TRANSIC is shown in Fig. 2. At a high level, after training the base policy in simulation, we deploy it on the real robot while monitored by a human operator. The human interrupts the autonomous execution when necessary and provides online correction through teleoperation. Such intervention and online correction are collected to train a residual policy, after which both base and residual policies are deployed to complete contact-rich manipulation tasks. In this section, we first elaborate on the simulation training phase with several important design choices that reduce sim-to-real gaps before transfer. We then introduce residual policies learned from human intervention and online correction. Subsequently, we present an integrated framework for deploying the base policy alongside the learned residual policy during testing. Finally, we provide implementation details.

3.1 Learning Base Policies in Simulation with RL