Probabilistic Dual-Space Fusion for Real-Time Human-Robot Interaction.

ABSTRACT: For robots in human environments, learning complex and demanding interaction skills from humans and responding quickly to human motions are highly desirable. A common challenge for interaction tasks is that the robot has to satisfy both the task space and the joint space constraints on its motion trajectories in real time. Few studies have addressed the issue of hyperspace constraints in human-robot interaction, whereas researchers have investigated it in robot imitation learning. In this work, we propose a method of dual-space feature fusion to enhance the accuracy of the inferred trajectories in both task space and joint space; then, we introduce a linear mapping operator (LMO) to map the inferred task space trajectory to a joint space trajectory. Finally, we combine the dual-space fusio

SUBMITTER: Li Y 

PROVIDER: S-EPMC10604510 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature