Can DaSH learn multi-robot strategies?
Encoding Reusable Multi-Robot Planning Strategies as Abstract Hypergraphs
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Making multi-robot planning more efficient. This paper introduces a method to extract reusable strategies from successful multi-robot plans, allowing robots to learn from past experiences and solve similar problems faster.
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Abstracting away details for generalizability. The key idea is to represent plans as "abstract hypergraphs" which capture the high-level structure of a solution without robot-specific or object-specific details. This allows the same abstract plan to be applied to different problems with varying robot capabilities, object arrangements, and even a different number of robots. This approach is particularly relevant for LLM-based systems as it offers a way to translate high-level LLM instructions into concrete, adaptable plans for multi-agent execution.