3.6ROJan 17, 2023
Show me what you want: Inverse reinforcement learning to automatically design robot swarms by demonstrationIlyes Gharbi, Jonas Kuckling, David Garzón Ramos et al.
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.
2.2ROMar 3
Generative adversarial imitation learning for robot swarms: Learning from human demonstrations and trained policiesMattes Kraus, Jonas Kuckling
In imitation learning, robots are supposed to learn from demonstrations of the desired behavior. Most of the work in imitation learning for swarm robotics provides the demonstrations as rollouts of an existing policy. In this work, we provide a framework based on generative adversarial imitation learning that aims to learn collective behaviors from human demonstrations. Our framework is evaluated across six different missions, learning both from manual demonstrations and demonstrations derived from a PPO-trained policy. Results show that the imitation learning process is able to learn qualitatively meaningful behaviors that perform similarly well as the provided demonstrations. Additionally, we deploy the learned policies on a swarm of TurtleBot 4 robots in real-robot experiments. The exhibited behaviors preserved their visually recognizable character and their performance is comparable to the one achieved in simulation.
6.7ROMar 10
On the Cost of Evolving Task Specialization in Multi-Robot SystemsPaolo Leopardi, Heiko Hamann, Jonas Kuckling et al.
Task specialization can lead to simpler robot behaviors and higher efficiency in multi-robot systems. Previous works have shown the emergence of task specialization during evolutionary optimization, focusing on feasibility rather than costs. In this study, we take first steps toward a cost-benefit analysis of task specialization in robot swarms using a foraging scenario. We evolve artificial neural networks as generalist behaviors for the entire task and as task-specialist behaviors for subtasks within a limited evaluation budget. We show that generalist behaviors can be successfully optimized while the evolved task-specialist controllers fail to cooperate efficiently, resulting in worse performance than the generalists. Consequently, task specialization does not necessarily improve efficiency when optimization budget is limited.
6.0ROJun 23
The Evaluation Cost of Task Specialization in Evolutionary Multi-Robot SystemsPaolo Leopardi, Heiko Hamann, Jonas Kuckling et al.
Task specialization can improve the efficiency of multi-robot systems (MRSs). Previous works have investigated the emergence of task-specialist robot controllers through evolutionary optimization and have argued that task specialization is more likely to evolve when subtask behaviors are readily available as building blocks. However, the available evaluation budget must be distributed across all subtasks, whereas a single generalist behavior can exploit the entire budget for its own optimization. We present a cost-benefit analysis of evolving task-specialist versus generalist behaviors in a foraging scenario here. In a physics-based robotics simulator, we study the total evaluation budget required to evolve task-specialist behaviors that outperform generalist behaviors across MRS sizes. We show that with increasing MRS size, a lower total evaluation budget is sufficient to evolve specialists that outperform generalists.