mlsanityData to 7 Oct 2026

Motivating Physical Activity via Competitive Human-Robot Interaction

arXiv 2202.07068 ↗First posted 14 Feb 2022cs.RO cs.AI cs.HCcited by 3

Not on the map. Why?

Abstract (arXiv)

This project aims to motivate research in competitive human-robot interaction by creating a robot competitor that can challenge human users in certain scenarios such as physical exercise and games. With this goal in mind, we introduce the Fencing Game, a human-robot competition used to evaluate both the capabilities of the robot competitor and user experience. We develop the robot competitor through iterative multi-agent reinforcement learning and show that it can perform well against human competitors. Our user study additionally found that our system was able to continuously create challenging and enjoyable interactions that significantly increased human subjects' heart rates. The majority of human subjects considered the system to be entertaining and desirable for improving the quality of their exercise.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 16 of its 55 references that are in the map. First posted on arXiv 14 Feb 2022.

Builds on

16 of 55 references.

References · 16

  1. 2015Continuous control with deep reinforcement learningcited by 3,279
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  3. 2019Emergent Tool Use From Multi-Agent Autocurriculacited by 308
  4. 2017Learning with Opponent-Learning Awarenesscited by 218
  5. 2015Multiagent Cooperation and Competition with Deep Reinforcement Learningcited by 180
  6. 2017Emergence of Locomotion Behaviours in Rich Environmentscited by 159
  7. 2017Cycles in adversarial regularized learningcited by 122
  8. 2017Emergent Complexity via Multi-Agent Competitioncited by 117
  9. 2016Opponent Modeling in Deep Reinforcement Learningcited by 94
  10. 2019Emergent Coordination Through Competitioncited by 57
  11. 2018Trust-Aware Decision Making for Human-Robot Collaboration: Model Learning and Planningcited by 32
  12. 2017Game-Theoretic Modeling of Human Adaptation in Human-Robot Collaborationcited by 21
4 more
  1. 2020LESS is More: Rethinking Probabilistic Models of Human Behaviorcited by 17
  2. 2018A Generalised Method for Empirical Game Theoretic Analysiscited by 15
  3. 2020Telemanipulation with Chopsticks: Analyzing Human Factors in User Demonstrationscited by 5
  4. 2020Contact-less manipulation of millimeter-scale objects via ultrasonic levitationcited by 2

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 1.

3 papers.

  1. 2023MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulationcited by 27
  2. 2024Self-Explainable Affordance Learning with Embodied Captioncited by 4
  3. 2023Stackelberg Games for Learning Emergent Behaviors During Competitive Autocurriculacited by 3