To enable safe and efficient human-robot collaboration in shared workspacesit is important for the robot to predict how a human will move when performinga task. While predicting human motion for tasks not known a priori is verychallenging, we argue that single-arm reaching motions for known tasks incollaborative settings (which are especially relevant for manufacturing) areindeed predictable. Two hypotheses underlie our approach for predicting suchmotions: First, that the trajectory the human performs is optimal with respectto an unknown cost function, and second, that human adaptation to theirpartner's motion can be captured well through iterative re-planning with theabove cost function. The key to our approach is thus to learn a cost functionwhich "explains" the motion of the human. To do this, we gather exampletrajectories from pairs of participants performing a collaborative assemblytask using motion capture. We then use Inverse Optimal Control to learn a costfunction from these trajectories. Finally, we predict reaching motions from thehuman's current configuration to a task-space goal region by iterativelyre-planning a trajectory using the learned cost function. Our planningalgorithm is based on the trajectory optimizer STOMP, it plans for a 23 DoFhuman kinematic model and accounts for the presence of a moving collaboratorand obstacles in the environment. Our results suggest that in most cases, ourmethod outperforms baseline methods when predicting motions. We also show thatour method outperforms baselines for predicting human motion when a human and arobot share the workspace.
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