Learn to Adapt to Human Walking: A Model-Based Reinforcement Learning Approach for a Robotic Assistant Rollator

Abstract

In this letter, we tackle the problem of adapting the motion of a robotic assistant rollator to patients with different mobility status. The goal is to achieve a coupled human–robot motion in a front-following setting as if the patient was pushing the rollator himself/herself. To this end, we propose a novel approach using model-based reinforcement learning (MBRL) for adapting the control policy of the robotic assistant. This approach encapsulates our previous work on human tracking and gait analysis from RGB-D and laser streams into a human-in-the-loop decision making strategy. We use long short-term memory (LSTM) networks for designing a human motion intention model and a coupling parameters forecast model, leveraging on the outcome of human gait analysis. An initial LSTM-based policy network was trained via imitation learning from human demonstrations in a motion capture setup. This policy is then fine-tuned with the MBRL framework using tracking data from real patients. A thorough evaluation analysis proves the efficiency of the MBRL approach as a user-adaptive controller.