# Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration

arXiv: https://arxiv.org/abs/2103.04077 · HTML: https://mlsanity.com/paper/2103.04077/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 44 references that are in the map.
- First posted on arXiv 6 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [Establishing Appropriate Trust via Critical States](https://mlsanity.com/paper/1810.08174/) (2018)
- [Reasoning about Hypothetical Agent Behaviours and their Parameters](https://mlsanity.com/paper/1906.11064/) (2019)
- [Game-Theoretic Modeling of Human Adaptation in Human-Robot Collaboration](https://mlsanity.com/paper/1701.07790/) (2017)
- [Expressing Robot Incapability](https://mlsanity.com/paper/1810.08167/) (2018)
- [Getting to Know One Another: Calibrating Intent, Capabilities and Trust for Human-Robot Collaboration](https://mlsanity.com/paper/2008.00699/) (2020)
- [Communicating Robot Arm Motion Intent Through Mixed Reality Head-mounted Displays](https://mlsanity.com/paper/1708.03655/) (2017)
- [Robot Capability and Intention in Trust-based Decisions across Tasks](https://mlsanity.com/paper/1909.05329/) (2019)

## Built on by (most cited first, 4 of 4)

- [PhysVLM: Enabling Visual Language Models to Understand Robotic Physical Reachability](https://mlsanity.com/paper/2503.08481/) (2025)
- [GOMA: Proactive Embodied Cooperative Communication via Goal-Oriented Mental Alignment](https://mlsanity.com/paper/2403.11075/) (2024)
- [On the Emergence of Symmetrical Reality](https://mlsanity.com/paper/2401.15132/) (2024)
- [Boltzmann State-Dependent Rationality](https://mlsanity.com/paper/2404.17725/) (2024)

## Abstract (arXiv)

Aligning humans' assessment of what a robot can do with its true capability is crucial for establishing a common ground between human and robot partners when they collaborate on a joint task. In this work, we propose an approach to calibrate humans' estimate of a robot's reachable workspace through a small number of demonstrations before collaboration. We develop a novel motion planning method, REMP, which jointly optimizes the physical cost and the expressiveness of robot motion to reveal the robot's reachability to a human observer. Our experiments with human participants demonstrate that a short calibration using REMP can effectively bridge the gap between what a non-expert user thinks a robot can reach and the ground truth. We show that this calibration procedure not only results in better user perception, but also promotes more efficient human-robot collaborations in a subsequent joint task.