Inverse Reinforcement Learning with Multi-Relational Chains for Robot-Centered Smart Home

TL;DR

This paper presents a novel inverse reinforcement learning approach using multi-relational chains to enable robots to imitate operator behaviors and adapt to dynamic smart home environments effectively.

Contribution

It introduces a multi-relational chain model combined with inverse reinforcement learning for robot behavior imitation in smart homes, improving adaptability and accuracy.

Findings

Handles dynamic environments effectively

Improves accuracy of robot action selection

Outperforms baseline behavior recording methods

Abstract

In a robot-centered smart home, the robot observes the home states with its own sensors, and then it can change certain object states according to an operator's commands for remote operations, or imitate the operator's behaviors in the house for autonomous operations. To model the robot's imitation of the operator's behaviors in a dynamic indoor environment, we use multi-relational chains to describe the changes of environment states, and apply inverse reinforcement learning to encoding the operator's behaviors with a learned reward function. We implement this approach with a mobile robot, and do five experiments to include increasing training days, object numbers, and action types. Besides, a baseline method by directly recording the operator's behaviors is also implemented, and comparison is made on the accuracy of home state evaluation and the accuracy of robot action selection. The results show that the proposed approach handles dynamic environment well, and guides the robot's actions in the house more accurately.