Hierarchical Human-Motion Prediction and Logic-Geometric Programming for Minimal Interference Human-Robot Tasks
This addresses coordination challenges in human-robot collaboration for manipulation tasks, representing an incremental improvement.
The paper tackles human-robot coordination in manipulation tasks by integrating hierarchical human motion prediction with a dynamic task and motion planning algorithm, achieving improved performance on the MoGaze dataset.
In this paper, we tackle the problem of human-robot coordination in sequences of manipulation tasks. Our approach integrates hierarchical human motion prediction with Task and Motion Planning (TAMP). We first devise a hierarchical motion prediction approach by combining Inverse Reinforcement Learning and short-term motion prediction using a Recurrent Neural Network. In a second step, we propose a dynamic version of the TAMP algorithm Logic-Geometric Programming (LGP). Our version of Dynamic LGP, replans periodically to handle the mismatch between the human motion prediction and the actual human behavior. We assess the efficacy of the approach by training the prediction algorithms and testing the framework on the publicly available MoGaze dataset.