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

arXiv: https://arxiv.org/abs/2310.03314 · HTML: https://mlsanity.com/paper/2310.03314/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 5 Oct 2023.

## Builds on (most cited first, 12 of 12)

- [A Spatio-temporal Transformer for 3D Human Motion Prediction](https://mlsanity.com/paper/2004.08692/) (2020)
- [Space-Time-Separable Graph Convolutional Network for Pose Forecasting](https://mlsanity.com/paper/2110.04573/) (2021)
- [mm-Pose: Real-Time Human Skeletal Posture Estimation using mmWave Radars and CNNs](https://mlsanity.com/paper/1911.09592/) (2019)
- [A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges](https://mlsanity.com/paper/2006.09319/) (2020)
- [TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild](https://mlsanity.com/paper/2104.04029/) (2021)
- [Socially and Contextually Aware Human Motion and Pose Forecasting](https://mlsanity.com/paper/2007.06843/) (2020)
- [Can We Use Diffusion Probabilistic Models for 3D Motion Prediction?](https://mlsanity.com/paper/2302.14503/) (2023)
- [Reliable Neural Networks for Regression Uncertainty Estimation](https://mlsanity.com/paper/2109.08213/) (2021)
- [MESSY Estimation: Maximum-Entropy based Stochastic and Symbolic densitY Estimation](https://mlsanity.com/paper/2306.04120/) (2023)
- [Goal Set Inverse Optimal Control and Iterative Re-planning for Predicting Human Reaching Motions in Shared Workspaces](https://mlsanity.com/paper/1606.02111/) (2016)
- [I-Planner: Intention-Aware Motion Planning Using Learning Based Human Motion Prediction](https://mlsanity.com/paper/1608.04837/) (2016)
- [Risk Bounded Nonlinear Robot Motion Planning With Integrated Perception & Control](https://mlsanity.com/paper/2201.01483/) (2022)

## Built on by (most cited first, 6 of 6)

- [Relevance for Human Robot Collaboration](https://mlsanity.com/paper/2409.07753/) (2024)
- [A Robust Filter for Marker-less Multi-person Tracking in Human-Robot Interaction Scenarios](https://mlsanity.com/paper/2406.01832/) (2024)
- [Relevance-driven Decision Making for Safer and More Efficient Human Robot Collaboration](https://mlsanity.com/paper/2409.13998/) (2024)
- [MDMP: Multi-modal Diffusion for supervised Motion Predictions with uncertainty](https://mlsanity.com/paper/2410.03860/) (2024)
- [Perceive What Matters: Relevance-Driven Scheduling for Multimodal Streaming Perception](https://mlsanity.com/paper/2603.13176/) (2026)
- [Adaptive Interaction Network for Human Motion Prediction During Human-Robot Collaboration](https://mlsanity.com/paper/2610.16624/) (2026)

## Abstract (arXiv)

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.