# Intent-Handover: Grounding Language in Human-Usage Regions for Trustworthy Robot-to-Human Handovers

arXiv: https://arxiv.org/abs/2503.03579 · HTML: https://mlsanity.com/paper/2503.03579/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 5 Mar 2025.

## Builds on (most cited first, 11 of 11)

- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [GRAB: A Dataset of Whole-Body Human Grasping of Objects](https://mlsanity.com/paper/2008.11200/) (2020)
- [ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation](https://mlsanity.com/paper/2204.13662/) (2022)
- [H2O: Two Hands Manipulating Objects for First Person Interaction Recognition](https://mlsanity.com/paper/2104.11181/) (2021)
- [Text2HOI: Text-guided 3D Motion Generation for Hand-Object Interaction](https://mlsanity.com/paper/2404.00562/) (2024)
- [FastVLM: Efficient Vision Encoding for Vision Language Models](https://mlsanity.com/paper/2412.13303/) (2024)
- [Object Handovers: a Review for Robotics](https://mlsanity.com/paper/2007.12952/) (2020)
- [Multi-GraspLLM: A Multimodal LLM for Multi-Hand Semantic Guided Grasp Generation](https://mlsanity.com/paper/2412.08468/) (2024)
- [Affordance-Aware Handovers with Human Arm Mobility Constraints](https://mlsanity.com/paper/2010.15436/) (2020)
- [Exploring Large Language Models to Facilitate Variable Autonomy for Human-Robot Teaming](https://mlsanity.com/paper/2312.07214/) (2023)
- [ContactHandover: Contact-Guided Robot-to-Human Object Handover](https://mlsanity.com/paper/2404.01402/) (2024)

## Abstract (arXiv)

Spoken instructions in robot-to-human handovers may specify either an object ("the cup") or an intended use ("pour water"); in both cases, successful handover requires the robot to infer the target object and the region remaining available for the human to hold. If the robot grasps that hold region, the object could become awkward to receive and immediately use, potentially reducing perceived competence and trust; if the gripper approaches too close to the receiving hand during delivery, perceived safety may also suffer. We present Intent-Handover, which grounds unconstrained speech and visual scene context into explicit grasp and delivery constraints. Given a spoken instruction and a scene observation, a vision-language model identifies the target object and the intended human-usage region. A grasp optimization module then selects a feasible grasp keeping this region accessible while enforcing clearance from the predicted receiving hand. During execution, the robot tracks upper-body key points to estimate the user's receiving pose and places the handover at an ergonomically feasible location. In a within-subjects ablation study (n=30), human-usage region awareness increases perceived trust, hand-gripper collision avoidance increases perceived safety, and interaction comfort is highest when both are enabled. Website and code: https://robot-future.github.io/intent-handover/.