Spiking Neural Networks as a Controller for Emergent Swarm Agents

Drones which can swarm and loiter in a certain area cost hundreds of dollars, but mosquitos can do the same and are essentially worthless. To control swarms of low-cost robots, researchers may end up spending countless hours brainstorming robot configurations and policies to ``organically" create behaviors which do not need expensive sensors and perception. Existing research explores the possible emergent behaviors in swarms of robots with only a binary sensor and a simple but hand-picked controller structure. Even agents in this highly limited sensing, actuation, and computational capability class can exhibit relatively complex global behaviors such as aggregation, milling, and dispersal, but finding the local interaction rules that enable more collective behaviors remains a significant challenge. This paper investigates the feasibility of training spiking neural networks to find those local interaction rules that result in particular emergent behaviors. In this paper, we focus on simulating a specific milling behavior already known to be producible using very simple binary sensing and acting agents. To do this, we use evolutionary algorithms to evolve not only the parameters (the weights, biases, and delays) of a spiking neural network, but also its structure. To create a baseline, we also show an evolutionary search strategy over the parameters for the incumbent hand-picked binary controller structure. Our simulations show that spiking neural networks can be evolved in binary sensing agents to form a mill.

Agent-Based Emulation for Deploying Robot Swarm Behaviors

Despite significant research, robotic swarms have yet to be useful in solving real-world problems, largely due to the difficulty of creating and controlling swarming behaviors in multi-agent systems. Traditional top-down approaches in which a desired emergent behavior is produced often require complex, resource-heavy robots, limiting their practicality. This paper introduces a bottom-up approach by employing an Embodied Agent-Based Modeling and Simulation approach, emphasizing the use of simple robots and identifying conditions that naturally lead to self-organized collective behaviors. Using the Reality-to-Simulation-to-Reality for Swarms (RSRS) process, we tightly integrate real-world experiments with simulations to reproduce known swarm behaviors as well as discovering a novel emergent behavior without aiming to eliminate or even reduce the sim2real gap. This paper presents the development of an Agent-Based Embodiment and Emulation process that balances the importance of running physical swarming experiments and the prohibitively time-consuming process of even setting up and running a single experiment with 20+ robots by leveraging low-fidelity lightweight simulations to enable hypothesis-formation to guide physical experiments. We demonstrate the usefulness of our methods by emulating two known behaviors from the literature and show a third behavior `discovered' by accident.

Representation Alignment from Human Feedback for Cross-Embodiment Reward Learning from Mixed-Quality Demonstrations

We study the problem of cross-embodiment inverse reinforcement learning, where we wish to learn a reward function from video demonstrations in one or more embodiments and then transfer the learned reward to a different embodiment (e.g., different action space, dynamics, size, shape, etc.). Learning reward functions that transfer across embodiments is important in settings such as teaching a robot a policy via human video demonstrations or teaching a robot to imitate a policy from another robot with a different embodiment. However, prior work has only focused on cases where near-optimal demonstrations are available, which is often difficult to ensure. By contrast, we study the setting of cross-embodiment reward learning from mixed-quality demonstrations. We demonstrate that prior work struggles to learn generalizable reward representations when learning from mixed-quality data. We then analyze several techniques that leverage human feedback for representation learning and alignment to enable effective cross-embodiment learning. Our results give insight into how different representation learning techniques lead to qualitatively different reward shaping behaviors and the importance of human feedback when learning from mixed-quality, mixed-embodiment data.

Exploring Behavior Discovery Methods for Heterogeneous Swarms of Limited-Capability Robots

We study the problem of determining the emergent behaviors that are possible given a functionally heterogeneous swarm of robots with limited capabilities. Prior work has considered behavior search for homogeneous swarms and proposed the use of novelty search over either a hand-specified or learned behavior space followed by clustering to return a taxonomy of emergent behaviors to the user. In this paper, we seek to better understand the role of novelty search and the efficacy of using clustering to discover novel emergent behaviors. Through a large set of experiments and ablations, we analyze the effect of representations, evolutionary search, and various clustering methods in the search for novel behaviors in a heterogeneous swarm. Our results indicate that prior methods fail to discover many interesting behaviors and that an iterative human-in-the-loop discovery process discovers more behaviors than random search, swarm chemistry, and automated behavior discovery. The combined discoveries of our experiments uncover 23 emergent behaviors, 18 of which are novel discoveries. To the best of our knowledge, these are the first known emergent behaviors for heterogeneous swarms of computation-free agents. Videos, code, and appendix are available at the project website: https://sites.google.com/view/heterogeneous-bd-methods

Swarm Mechanics and Swarm Chemistry: A Transdisciplinary Approach for Robot Swarms

This paper for the first time attempts to bridge the knowledge between chemistry, fluid mechanics, and robot swarms. By forming these connections, we attempt to leverage established methodologies and tools from these these domains to uncover how we can better comprehend swarms. The focus of this paper is in presenting a new framework and sharing the reasons we find it promising and exciting. While the exact methods are still under development, we believe simply laying out a potential path towards solutions that have evaded our traditional methods using a novel method is worth considering. Our results are characterized through both simulations and real experiments on ground robots.