Enhanced Human-Robot Collaboration using Constrained Probabilistic
  Human-Motion Prediction
Aadi Kothari Tony Tohme Xiaotong Zhang Kamal Youcef-Toumi
Human motion prediction is an essential step for efficient and safe
human-robot collaboration. Current methods either purely rely on representing
the human joints in some form of neural network-based architecture or use
regression models offline to fit hyper-parameters in the hope of capturing a
model encompassing human motion. While these methods provide good initial
results, they are missing out on leveraging well-studied human body kinematic
models as well as body and scene constraints which can help boost the efficacy
of these prediction frameworks while also explicitly avoiding implausible human
joint configurations. We propose a novel human motion prediction framework that
incorporates human joint constraints and scene constraints in a Gaussian
Process Regression (GPR) model to predict human motion over a set time horizon.
This formulation is combined with an online context-aware constraints model to
leverage task-dependent motions. It is tested on a human arm kinematic model
and implemented on a human-robot collaborative setup with a UR5 robot arm to
demonstrate the real-time capability of our approach. Simulations were also
performed on datasets like HA4M and ANDY. The simulation and experimental
results demonstrate considerable improvements in a Gaussian Process framework
when these constraints are explicitly considered.