# DTRT: Enhancing Human Intent Estimation and Role Allocation for Physical Human-Robot Collaboration

arXiv: https://arxiv.org/abs/2505.17490 · HTML: https://mlsanity.com/paper/2505.17490/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 23 May 2025.

## Builds on (most cited first, 6 of 6)

- [Transformer Networks for Trajectory Forecasting](https://mlsanity.com/paper/2003.08111/) (2020)
- [Robot Trajectron: Trajectory Prediction-based Shared Control for Robot Manipulation](https://mlsanity.com/paper/2402.02499/) (2024)
- [Constraint-Aware Intent Estimation for Dynamic Human-Robot Object Co-Manipulation](https://mlsanity.com/paper/2409.00215/) (2024)
- [Towards Proactive Safe Human-Robot Collaborations via Data-Efficient Conditional Behavior Prediction](https://mlsanity.com/paper/2311.11893/) (2023)
- [Modeling and analysis of pHRI with Differential Game Theory](https://mlsanity.com/paper/2307.10739/) (2023)
- [Predicting human motion intention for pHRI assistive control](https://mlsanity.com/paper/2307.10743/) (2023)

## Built on by (most cited first, 1 of 1)

- [Generative Action-Chunk Sampling for Adaptive Stiffness Control in Physical Human-Robot Collaboration](https://mlsanity.com/paper/2608.25284/) (2026)

## Abstract (arXiv)

In physical Human-Robot Collaboration (pHRC), accurate human intent estimation and rational human-robot role allocation are crucial for safe and efficient assistance. Existing methods that rely on short-term motion data for intention estimation lack multi-step prediction capabilities, hindering their ability to sense intent changes and adjust human-robot assignments autonomously, resulting in potential discrepancies. To address these issues, we propose a Dual Transformer-based Robot Trajectron (DTRT) featuring a hierarchical architecture, which harnesses human-guided motion and force data to rapidly capture human intent changes, enabling accurate trajectory predictions and dynamic robot behavior adjustments for effective collaboration. Specifically, human intent estimation in DTRT uses two Transformer-based Conditional Variational Autoencoders (CVAEs), incorporating robot motion data in obstacle-free case with human-guided trajectory and force for obstacle avoidance. Additionally, Differential Cooperative Game Theory (DCGT) is employed to synthesize predictions based on human-applied forces, ensuring robot behavior align with human intention. Compared to state-of-the-art (SOTA) methods, DTRT incorporates human dynamics into long-term prediction, providing an accurate understanding of intention and enabling rational role allocation, achieving robot autonomy and maneuverability. Experiments demonstrate DTRT's accurate intent estimation and superior collaboration performance.