mlsanityData to 7 Oct 2026

Show me what you want: Inverse reinforcement learning to automatically design robot swarms by demonstration

arXiv 2301.06864 ↗First posted 17 Jan 2023cs.RO cs.LGcited by 1

On the map: Offline RL and imitation learning from data → Inverse reinforcement learning

Abstract (arXiv)

Automatic design is a promising approach to generating control software for robot swarms. So far, automatic design has relied on mission-specific objective functions to specify the desired collective behavior. In this paper, we explore the possibility to specify the desired collective behavior via demonstrations. We develop Demo-Cho, an automatic design method that combines inverse reinforcement learning with automatic modular design of control software for robot swarms. We show that, only on the basis of demonstrations and without the need to be provided with an explicit objective function, Demo-Cho successfully generated control software to perform four missions. We present results obtained in simulation and with physical robots.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 6 of its 58 references that are in the map. First posted on arXiv 17 Jan 2023.

Builds on

6 of 58 references.

References · 6

  1. 2018Multi-Agent Generative Adversarial Imitation Learningcited by 99
  2. 2016Inverse Reinforcement Learning in Swarm Systemscited by 18
  3. 2017Embodied Evolution in Collective Robotics: A Reviewcited by 15
  4. 2015Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robotscited by 5
  5. 2016Turing learning: a metric-free approach to inferring behavior and its application to swarmscited by 4
  6. 2022Understandable Controller Extraction from Video Observations of Swarmscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Generative adversarial imitation learning for robot swarms: Learning from human demonstrations and trained policiescited by 0

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Close in wording, no citation either way

  1. 2024Learning to Imitate Spatial Organization in Multi-robot Systems
  2. 2025Symmetry-Guided Multi-Agent Inverse Reinforcement Learning
  3. 2024Stable Inverse Reinforcement Learning: Policies from Control Lyapunov Landscapes
  4. 2022Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations
  5. 2023Discovering Individual Rewards in Collective Behavior through Inverse Multi-Agent Reinforcement Learning
  6. 2018Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications
  7. 2022Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning
  8. 2023Imitation Learning based Alternative Multi-Agent Proximal Policy Optimization for Well-Formed Swarm-Oriented Pursuit Avoidance