Trajectory generation under the least action principle for physical human-robot cooperation

Trajectory generation for active physical assistance to humans in cooperative haptic tasks gains increasing interest in recent literature. Planning-based approaches represent one class of trajectory synthesis methods for active robotic partners. To overcome the limitations of kinematic planning algorithms in dynamic tasks, we propose a three-step approach to the synthesis of trajectories under the principle of least action. This is motivated by neuroscientific findings on human effort minimization in motor tasks. A trajectory is generated by optimized sequencing of optimal motion primitives. The benefits of the proposed method for physical human-robot cooperation are demonstrated in human user studies in a 2D cooperative transport task in a virtual maze.