Efficient Sim-to-real Transfer of Contact-Rich Manipulation Skills with Online Admittance Residual Learning

Abstract

Learning contact-rich manipulation skills is essential to robotic applications. Such skills require the robots to interact with the environment with feasible manipulation trajectories and suitable compliance control parameters to enable safe and stable contact. However, learning these skills is challenging due to data inefficiency in the real world and the sim-to-real gap in simulation. In this paper, we introduce a hybrid offline-online framework to learn robust manipulation skills. We employ model-free reinforcement learning for the offline phase to obtain the robot motion and compliance control parameters in simulation. Subsequently, in the online phase, we learn the residual of the compliance control parameters to maximize robot performance-related criteria with force sensor measurements in real time. To demonstrate the effectiveness and robustness of our approach, we provide comparative results against existing methods for assembly and pivoting tasks.

Framework Overview

We propose a framework to learn robot manipulation skills that can transfer to the real world. The framework contains two phases: skill learning in simulation and admittance adaptation on the real robot. We use model-free RL to learn the robot's motion with domain randomization to enhance the robustness for direct transfer. The compliance control parameters are learned at the same time and serve as an initialization to online admittance learning. During online execution, we iteratively learn the residual of the admittance control parameters by optimizing the future robot trajectory smoothness and task completion criterion. We conduct real-world experiments on two typical contact-rich manipulation tasks: assembly and pivoting. Our proposed framework achieves efficient transfer from simulation to the real world. Furthermore, it shows excellent generalization ability in tasks with different kinematic or dynamic properties.

Learned Polices in Simulation

Real World Experiments: Sim-to-real Transfer

Real World Experiments: Assembly  Tasks Generalization 

Real World Experiments: Pivoting Tasks Generalization 

Ablation on Weight Parameter Selection

We study the effect of weight parameter selection in online admittance residual learning. We find smaller weights tend to prioritize trajectory smoothness, and larger weight values prioritize task completion.  

Additional Experiments: Screwing

We additionally evaluate the effectiveness of the framework on a screwing task.  We directly applied the learned policy for the square peg-in-hole with the proposed online admittance learning method on an M10 bolt-nut assembly task and found it can robustly align the bolt with the nut. Therefore, instead of retraining from scratch, we directly utilized this policy and added a rotation primitive for screwing. The entire process, the online admittance learning, is constantly optimized for the admittance controller. Surprisingly, we can robustly align the nut and bolt with the proposed approach and stably screw it. 

Ablation on smaller clearances

Ablation on reward function design

Ablation on model-free RL algorithms

Comparison of Contact Force

Additional Study: Comparison of External Force Estimation 

Here we compare the performance of using different external force estimation/modeling methods. The task is to let an admittance-controlled robot move to a table to establish contact. The objective function is purely the FITAVE objective to ensure the smoothness of the trajectory and reduce the oscillation. We found the 'record & replay' strategy performs better than using online data to fit a force model.

Additional Study: Utilizing Online Admittance Learning for Obtaining Optimal Admittance/Impedance Control Parameters

Here we demonstrate the effectiveness of purely using online admittance learning with (random) initialized admittance control parameters. We let the robot contact the table with different materials. With randomly initialized parameters, the robot will oscillate and cannot stabilize itself. With online admittance learning, the robot can instantly find the optimal control parameters and eliminate the oscillation within one step .