# Affordance-Aware Handovers with Human Arm Mobility Constraints

arXiv: https://arxiv.org/abs/2010.15436 · HTML: https://mlsanity.com/paper/2010.15436/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2020.

## Built on by (most cited first, 8 of 8)

- [Learning Visual Affordance Grounding from Demonstration Videos](https://mlsanity.com/paper/2108.05675/) (2021)
- [HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers](https://mlsanity.com/paper/2205.09747/) (2022)
- [Building Affordance Relations for Robotic Agents - A Review](https://mlsanity.com/paper/2105.06706/) (2021)
- [ContactHandover: Contact-Guided Robot-to-Human Object Handover](https://mlsanity.com/paper/2404.01402/) (2024)
- [Interpretable Affordance Detection on 3D Point Clouds with Probabilistic Prototypes](https://mlsanity.com/paper/2504.18355/) (2025)
- [Affordance Labeling and Exploration: A Manifold-Based Approach](https://mlsanity.com/paper/2407.15479/) (2024)
- [Evaluation of Performance-Trust vs Moral-Trust Violation in 3D Environment](https://mlsanity.com/paper/2206.15430/) (2022)
- [Intent-Handover: Grounding Language in Human-Usage Regions for Trustworthy Robot-to-Human Handovers](https://mlsanity.com/paper/2503.03579/) (2025)

## Abstract (arXiv)

Reasoning about object handover configurations allows an assistive agent to estimate the appropriateness of handover for a receiver with different arm mobility capacities. While there are existing approaches for estimating the effectiveness of handovers, their findings are limited to users without arm mobility impairments and to specific objects. Therefore, current state-of-the-art approaches are unable to hand over novel objects to receivers with different arm mobility capacities. We propose a method that generalises handover behaviours to previously unseen objects, subject to the constraint of a user's arm mobility levels and the task context. We propose a heuristic-guided hierarchically optimised cost whose optimisation adapts object configurations for receivers with low arm mobility. This also ensures that the robot grasps consider the context of the user's upcoming task, i.e., the usage of the object. To understand preferences over handover configurations, we report on the findings of an online study, wherein we presented different handover methods, including ours, to 259 users with different levels of arm mobility. We find that people's preferences over handover methods are correlated to their arm mobility capacities. We encapsulate these preferences in a statistical relational model (SRL) that is able to reason about the most suitable handover configuration given a receiver's arm mobility and upcoming task. Using our SRL model, we obtained an average handover accuracy of 90.8% when generalising handovers to novel objects.