Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot Coordination

• URL: http://arxiv.org/abs/2501.06058v3
• Date: Wed, 05 Mar 2025 15:37:52 GMT
• Title: Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot Coordination
• Authors: Kevin Fu, Shalin Jain, Pierce Howell, Harish Ravichandar,
• Abstract summary: We propose Capability-Aware Shared Hypernetworks (CASH) to enable a single architecture to dynamically adapt to each robot and the current context.<n>CASH encodes shared decision making strategies that can be adapted to each robot based on local observations and the robots' individual and collective capabilities.
• Score: 2.681242476043447
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances have enabled heterogeneous multi-robot teams to learn complex and effective coordination. However, existing architectural designs that support heterogeneous teams tend to force a trade-off between expressivity and efficiency. Some attempt to encode diverse behaviors within a single shared architecture by appending the input with an ID unique to each robot or robot type. These designs improve sample and parameter efficiency but tend to limit behavioral diversity. Others use a separate policy for each robot, enabling greater diversity at the cost of efficiency and generalization. We view these two designs as ends of a spectrum and explore a middle-ground approach that enables efficient learning of diverse behaviors. Inspired by work in transfer learning and meta RL, and building upon prior work in trait-based task allocation, we propose Capability-Aware Shared Hypernetworks (CASH), a general-purpose soft weight sharing architecture that uses hypernetworks to enable a single architecture to dynamically adapt to each robot and the current context. Intuitively, CASH encodes shared decision making strategies that can be adapted to each robot based on local observations and the robots' individual and collective capabilities (e.g., speed and payload). CASH explicitly captures the impact of capabilities on collective behavior, enabling zero-shot generalization to unseen robots or team compositions. We conducted experiments across four heterogeneous coordination tasks and three learning paradigms (imitation learning, value-based, and policy-gradient RL) using SOTA multi-robot simulation (JaxMARL) and hardware (Robotarium) platforms. Across all conditions, CASH generates appropriately diverse behaviors and outperforms baseline architectures in task performance and sample efficiency during training and zero-shot generalization while utilizing 60%-80% fewer learnable parameters.

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