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We present a novel approach to generate collision-free trajectories for arobot operating in close proximity with a human obstacle in an occludedenvironment. The self-occlusions of the robot can significantly reduce theaccuracy of human motion prediction, and we present a novel deep learning-basedprediction algorithm. Our formulation uses CNNs and LSTMs and we augmenthuman-action datasets with synthetically generated occlusion information fortraining. We also present an occlusion-aware planner that uses our motionprediction algorithm to compute collision-free trajectories. We highlightperformance of the overall approach (HMPO) in complex scenarios and observeupto 68% performance improvement in motion prediction accuracy, and 38%improvement in terms of error distance between the ground-truth and thepredicted human joint positions.

HMPO: Human Motion Prediction in Occluded Environments for Safe Motion Planning