Log In Sign Up

Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertainty

by   Alireza Ranjbar, et al.

While classic control theory offers state of the art solutions in many problem scenarios, it is often desired to improve beyond the structure of such solutions and surpass their limitations. To this end, rpl offers a formulation to improve existing controllers with reinforcement learning (RL) by learning an additive "residual" to the output of a given controller. However, the applicability of such an approach highly depends on the structure of the controller. Often, internal feedback signals of the controller limit an RL algorithm to adequately change the policy and, hence, learn the task. We propose a new formulation that addresses these limitations by also modifying the feedback signals to the controller with an RL policy and show superior performance of our approach on a contact-rich peg-insertion task under position and orientation uncertainty. In addition, we use a recent impedance control architecture as control framework and show the difficulties of standard RPL. Furthermore, we introduce an adaptive curriculum for the given task to gradually increase the task difficulty in terms of position and orientation uncertainty. A video showing the results can be found at .


page 5

page 7


Learning Contact-Rich Manipulation Tasks with Rigid Position-Controlled Robots

To fully realize industrial automation, it is indispensable to give the ...

Residual Reinforcement Learning from Demonstrations

Residual reinforcement learning (RL) has been proposed as a way to solve...

Stability-Guaranteed Reinforcement Learning for Contact-rich Manipulation

Reinforcement learning (RL) has had its fair share of success in contact...

Modelling and Learning Dynamics for Robotic Food-Cutting

Data-driven approaches for modelling contact-rich tasks address many of ...

Proactive Action Visual Residual Reinforcement Learning for Contact-Rich Tasks Using a Torque-Controlled Robot

Contact-rich manipulation tasks are commonly found in modern manufacturi...

Learning Variable Impedance Control for Contact Sensitive Tasks

Reinforcement learning algorithms have shown great success in solving di...

A Learning-Based Estimation and Control Framework for Contact-Intensive Tight-Tolerance Tasks

We propose a novel data-driven estimation and control framework for cont...