mlsanityData to 14 Oct 2026

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

arXiv 2103.04077 ↗First posted 6 Mar 2021cs.RO cs.AIcited by 4

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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.

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

7 of 44 references.

References · 7

  1. 2018Establishing Appropriate Trust via Critical Statescited by 48
  2. 2019Reasoning about Hypothetical Agent Behaviours and their Parameterscited by 24
  3. 2017Game-Theoretic Modeling of Human Adaptation in Human-Robot Collaborationcited by 21
  4. 2018Expressing Robot Incapabilitycited by 13
  5. 2020Getting to Know One Another: Calibrating Intent, Capabilities and Trust for Human-Robot Collaborationcited by 7
  6. 2017Communicating Robot Arm Motion Intent Through Mixed Reality Head-mounted Displayscited by 6
  7. 2019Robot Capability and Intention in Trust-based Decisions across Taskscited by 6

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Citing papers per year: 2024: 3; 2025: 1.

4 papers.