Enhanced Human-Robot Collaboration using Constrained Probabilistic Human-Motion Prediction

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. These methods are also lacking on mechanisms to explicitly avoid 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, while considering associated measurement uncertainty, to predict human motion. This formulation is combined with an online context-aware constraint model to leverage task-dependent motions. Our emphasis on explicit constraint modeling differentiates this work from prior studies. The proposed approach is validated on a human arm kinematic model and implemented in a human-robot collaborative setup with a UR5 robot arm, demonstrating its real-time feasibility. Simulations show that our framework dramatically improves overall mean per joint position error by as much as 66% on HA4M dataset and 51% on Andy dataset, while negative log-likelihood on the predicted probability distribution function is also improved by 32% on HA4M dataset and 15% on Andy dataset when compared to baseline methods.