Robots That Can See: Leveraging Human Pose for Trajectory Prediction
Tim Salzmann Lewis Chiang Markus Ryll Dorsa Sadigh Carolina Parada Alex Bewley
Anticipating the motion of all humans in dynamic environments such as homes
and offices is critical to enable safe and effective robot navigation. Such
spaces remain challenging as humans do not follow strict rules of motion and
there are often multiple occluded entry points such as corners and doors that
create opportunities for sudden encounters. In this work, we present a
Transformer based architecture to predict human future trajectories in
human-centric environments from input features including human positions, head
orientations, and 3D skeletal keypoints from onboard in-the-wild sensory
information. The resulting model captures the inherent uncertainty for future
human trajectory prediction and achieves state-of-the-art performance on common
prediction benchmarks and a human tracking dataset captured from a mobile robot
adapted for the prediction task. Furthermore, we identify new agents with
limited historical data as a major contributor to error and demonstrate the
complementary nature of 3D skeletal poses in reducing prediction error in such
challenging scenarios.