Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed

TL;DR

This paper presents a comprehensive framework combining demonstration learning and reinforcement learning to develop adaptive tactile feedback models for reactive robot behaviors, enabling autonomous task success in changing environments.

Contribution

It introduces a full pipeline for learning and fine-tuning feedback models for reactive motion planning using demonstrations and reinforcement learning.

Findings

Successfully learned tactile feedback models on a real robot.

Efficiently adapted models to new task settings with few interactions.

Demonstrated improved reactive behaviors in unpredictable environments.

Abstract

Robots need to be able to adapt to unexpected changes in the environment such that they can autonomously succeed in their tasks. However, hand-designing feedback models for adaptation is tedious, if at all possible, making data-driven methods a promising alternative. In this paper we introduce a full framework for learning feedback models for reactive motion planning. Our pipeline starts by segmenting demonstrations of a complete task into motion primitives via a semi-automated segmentation algorithm. Then, given additional demonstrations of successful adaptation behaviors, we learn initial feedback models through learning from demonstrations. In the final phase, a sample-efficient reinforcement learning algorithm fine-tunes these feedback models for novel task settings through few real system interactions. We evaluate our approach on a real anthropomorphic robot in learning a tactile feedback task.