Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Learning to Imitate Spatial Organization in Multi-robot Systems

Ayomide O. Agunloye, Sarvapali D. Ramchurn, Mohammad D. Soorati

TL;DR

This work tackles reconstructing swarm collective behavior without access to swarm controllers by transforming expert demonstrations into informative multi-agent interaction features and recovering policies with Multi-Agent GAIL (MA-GAIL) using multiple discriminators and demonstration sharing. The method is evaluated on three spatial organization tasks (aggregation, homing, obstacle avoidance) by separating the environment into motion and control layers within a DEC-POMDP framework, achieving near-expert performance and demonstrating the value of environment-aware representations. Key findings include improved learning representation through feature transformation and robust imitation across diverse scenarios, while noting limitations in highly sparse-reward tasks and scalability with many discriminators. The work advances practical swarm analysis and testing by enabling behavior reconstruction and verification without direct access to the original swarm controllers.

Abstract

Understanding collective behavior and how it evolves is important to ensure that robot swarms can be trusted in a shared environment. One way to understand the behavior of the swarm is through collective behavior reconstruction using prior demonstrations. Existing approaches often require access to the swarm controller which may not be available. We reconstruct collective behaviors in distinct swarm scenarios involving shared environments without using swarm controller information. We achieve this by transforming prior demonstrations into features that describe multi-agent interactions before behavior reconstruction with multi-agent generative adversarial imitation learning (MA-GAIL). We show that our approach outperforms existing algorithms in spatial organization, and can be used to observe and reconstruct a swarm's behavior for further analysis and testing, which might be impractical or undesirable on the original robot swarm.

Learning to Imitate Spatial Organization in Multi-robot Systems

TL;DR

This work tackles reconstructing swarm collective behavior without access to swarm controllers by transforming expert demonstrations into informative multi-agent interaction features and recovering policies with Multi-Agent GAIL (MA-GAIL) using multiple discriminators and demonstration sharing. The method is evaluated on three spatial organization tasks (aggregation, homing, obstacle avoidance) by separating the environment into motion and control layers within a DEC-POMDP framework, achieving near-expert performance and demonstrating the value of environment-aware representations. Key findings include improved learning representation through feature transformation and robust imitation across diverse scenarios, while noting limitations in highly sparse-reward tasks and scalability with many discriminators. The work advances practical swarm analysis and testing by enabling behavior reconstruction and verification without direct access to the original swarm controllers.

Abstract

Understanding collective behavior and how it evolves is important to ensure that robot swarms can be trusted in a shared environment. One way to understand the behavior of the swarm is through collective behavior reconstruction using prior demonstrations. Existing approaches often require access to the swarm controller which may not be available. We reconstruct collective behaviors in distinct swarm scenarios involving shared environments without using swarm controller information. We achieve this by transforming prior demonstrations into features that describe multi-agent interactions before behavior reconstruction with multi-agent generative adversarial imitation learning (MA-GAIL). We show that our approach outperforms existing algorithms in spatial organization, and can be used to observe and reconstruct a swarm's behavior for further analysis and testing, which might be impractical or undesirable on the original robot swarm.
Paper Structure (15 sections, 3 equations, 4 figures, 1 algorithm)

This paper contains 15 sections, 3 equations, 4 figures, 1 algorithm.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORKS
  3. BACKGROUND
  4. METHOD
  5. Expert Demonstrations Transformation
  6. Policy Recovery with Multi-Agent GAIL
  7. EXPERIMENT
  8. Swarm Scenarios
  9. Simulation Environment
  10. Implementation Details
  11. Expert Demonstrations
  12. Learner UAVs
  13. Network Training
  14. RESULTS
  15. CONCLUSION

Figures (4)

  • Figure 1: Snapshot of the motion layer showing the obstacle avoidance behavior and position trace for Experts, Learners, and Random swarming UAVs between t=$0$ and t=$300$s. X represents UAVs positions at t=$0$. Blue boxes are inactive UAVs locations.
  • Figure 2: Boxplots of true episode rewards obtained in $200$ evaluation episodes by the proposed approach (MA-GAIL), BC, and PS-AIRL trained with $400$ expert demonstrations in all scenarios.
  • Figure 3: Boxplots of normalized reward values for Expert, Random, and MA-GAIL-400 over $200$ evaluation episodes initialized from random starting states in all scenarios.
  • Figure 4: Visualization of swarming UAVs positions for $10$ evaluation episodes in all scenarios. Area coverage of expert (top) and learner (bottom). Active UAV locations are marked by X and inactive locations are shown as O.