Correct Me if I am Wrong: Interactive Learning for Robotic Manipulation

TL;DR

This paper introduces CEILing, an interactive learning framework that enables robots to learn complex manipulation tasks efficiently from visual inputs with minimal real-world training time, leveraging human feedback.

Contribution

The paper proposes a novel interactive learning approach combining corrective and evaluative feedback for efficient robot manipulation learning from raw images.

Findings

CEILing achieves successful manipulation in less than one hour of real-world training.

The framework effectively integrates human feedback with autonomous experience.

Demonstrated success in both simulation and real-world experiments.

Abstract

Learning to solve complex manipulation tasks from visual observations is a dominant challenge for real-world robot learning. Although deep reinforcement learning algorithms have recently demonstrated impressive results in this context, they still require an impractical amount of time-consuming trial-and-error iterations. In this work, we consider the promising alternative paradigm of interactive learning in which a human teacher provides feedback to the policy during execution, as opposed to imitation learning where a pre-collected dataset of perfect demonstrations is used. Our proposed CEILing (Corrective and Evaluative Interactive Learning) framework combines both corrective and evaluative feedback from the teacher to train a stochastic policy in an asynchronous manner, and employs a dedicated mechanism to trade off human corrections with the robot's own experience. We present results obtained with our framework in extensive simulation and real-world experiments to demonstrate that CEILing can effectively solve complex robot manipulation tasks directly from raw images in less than one hour of real-world training.