# Constraint-Aware Intent Estimation for Dynamic Human-Robot Object Co-Manipulation

arXiv: https://arxiv.org/abs/2409.00215 · HTML: https://mlsanity.com/paper/2409.00215/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2024.

## Built on by (most cited first, 3 of 3)

- [Probabilistic Human Intent Prediction for Mobile Manipulation: An Evaluation with Human-Inspired Constraints](https://mlsanity.com/paper/2507.10131/) (2025)
- [DTRT: Enhancing Human Intent Estimation and Role Allocation for Physical Human-Robot Collaboration](https://mlsanity.com/paper/2505.17490/) (2025)
- [Learning from Humans for Proactive Assistance in Human-Robot Collaborative Transport](https://mlsanity.com/paper/2609.25351/) (2026)

## Abstract (arXiv)

Constraint-aware estimation of human intent is essential for robots to physically collaborate and interact with humans. Further, to achieve fluid collaboration in dynamic tasks intent estimation should be achieved in real-time. In this paper, we present a framework that combines online estimation and control to facilitate robots in interpreting human intentions, and dynamically adjust their actions to assist in dynamic object co-manipulation tasks while considering both robot and human constraints. Central to our approach is the adoption of a Dynamic Systems (DS) model to represent human intent. Such a low-dimensional parameterized model, along with human manipulability and robot kinematic constraints, enables us to predict intent using a particle filter solely based on past motion data and tracking errors. For safe assistive control, we propose a variable impedance controller that adapts the robot's impedance to offer assistance based on the intent estimation confidence from the DS particle filter. We validate our framework on a challenging real-world human-robot co-manipulation task and present promising results over baselines. Our framework represents a significant step forward in physical human-robot collaboration (pHRC), ensuring that robot cooperative interactions with humans are both feasible and effective.