TY - JOUR
T1 - Robots That Can See
T2 - Leveraging Human Pose for Trajectory Prediction
AU - Salzmann, Tim
AU - Chiang, Hao Tien Lewis
AU - Ryll, Markus
AU - Sadigh, Dorsa
AU - Parada, Carolina
AU - Bewley, Alex
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/11/1
Y1 - 2023/11/1
N2 - 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.
AB - 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.
KW - Autonomous vehicle navigation
KW - deep learning methods
KW - human-aware motion planning
UR - http://www.scopus.com/inward/record.url?scp=85171590242&partnerID=8YFLogxK
U2 - 10.1109/LRA.2023.3312035
DO - 10.1109/LRA.2023.3312035
M3 - Article
AN - SCOPUS:85171590242
SN - 2377-3766
VL - 8
SP - 7090
EP - 7097
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
IS - 11
ER -