Enhanced Human-Robot Collaboration using Constrained Probabilistic Human-Motion Prediction
This work addresses the need for more accurate and safe motion prediction in human-robot collaboration, though it is incremental by building on existing Gaussian Process methods with added constraints.
The paper tackles the problem of human motion prediction for safe human-robot collaboration by incorporating human joint and scene constraints into a Gaussian Process Regression model, resulting in considerable improvements in prediction efficacy as demonstrated in simulations and experiments with a UR5 robot arm.
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.