# Learning from Humans for Proactive Assistance in Human-Robot Collaborative Transport

arXiv: https://arxiv.org/abs/2609.25351 · HTML: https://mlsanity.com/paper/2609.25351/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 21 Sep 2026.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction](https://mlsanity.com/paper/1903.07933/) (2019)
- [MoGaze: A Dataset of Full-Body Motions that Includes Workspace Geometry and Eye-Gaze](https://mlsanity.com/paper/2011.11552/) (2020)
- [Diffusion Co-Policy for Synergistic Human-Robot Collaborative Tasks](https://mlsanity.com/paper/2305.12171/) (2023)
- [It Takes Two: Learning to Plan for Human-Robot Cooperative Carrying](https://mlsanity.com/paper/2209.12890/) (2022)
- [HARMONIC: A Multimodal Dataset of Assistive Human-Robot Collaboration](https://mlsanity.com/paper/1807.11154/) (2018)
- [Learning Human-Humanoid Coordination for Collaborative Object Carrying](https://mlsanity.com/paper/2510.14293/) (2025)
- [Human-Robot Collaborative Carrying of Objects with Unknown Deformation Characteristics](https://mlsanity.com/paper/2201.10392/) (2022)
- [Estimating Human Intent for Physical Human-Robot Co-Manipulation](https://mlsanity.com/paper/1705.10851/) (2017)
- [Constraint-Aware Intent Estimation for Dynamic Human-Robot Object Co-Manipulation](https://mlsanity.com/paper/2409.00215/) (2024)
- [Implicit Communication in Human-Robot Collaborative Transport](https://mlsanity.com/paper/2502.03346/) (2025)
- [Context-aware collaborative pushing of heavy objects using skeleton-based intention prediction](https://mlsanity.com/paper/2505.10239/) (2025)
- [Proactive Robot Control for Collaborative Manipulation Using Human Intent](https://mlsanity.com/paper/2311.02809/) (2023)
- [PAINT: Partner-Agnostic Intent-Aware Cooperative Transport with Legged Robots](https://mlsanity.com/paper/2604.12852/) (2026)
- [Safe Human-Robot Collaborative Transportation via Trust-Driven Role Adaptation](https://mlsanity.com/paper/2207.05896/) (2022)
- [Crafting with a Robot Assistant: Use Social Cues to Inform Adaptive Handovers in Human-Robot Collaboration](https://mlsanity.com/paper/2301.02938/) (2023)
- [Recognizing Intent in Collaborative Manipulation](https://mlsanity.com/paper/2308.09177/) (2023)

## Abstract (arXiv)

We focus on human-robot collaborative transport, a challenging task of broad relevance spanning logistics, manufacturing, and the home, in which a user and a robot work together to relocate a large or heavy object. To act as an effective partner, the robot should reduce the user's effort by contributing to efficient relocation of the object while remaining physically responsive to them. Prior work often addresses these capabilities separately, producing robots that may move the object efficiently but resist user input, or accommodate the user but depend on continuous guidance. Our key insight is that obstacle-constrained collaborative transport requires integrating predictions of human collaborative behavior with compliant robot control. To this end, we introduce PROACT, a framework for human-robot collaborative transport that incorporates anticipation into compliant whole-body control through a learned model of human collaborative behavior. Trained on a large-scale, real-world dataset of dyadic human transport demonstrations, our transformer architecture distills collaborative behavior into predictions of future object motion. Across 108 real-world trials with a 9-DoF mobile manipulator, PROACT reduces mean interaction work by 59.2% and 20.4%, and mean completion time by 12.9% and 6.9%, relative to compliance-only and MPC baselines, respectively. Footage from our experiments can be found at https://youtu.be/qAGvQfVPjbk.