OPAL: ロボット学習のための物理システムの因果的理解の符号化
OPAL: Encoding Causal Understanding of Physical Systems for Robot Learning
物理法則に基づくトポロジー制約をフローマッチングに導入した視覚-言語-行動アーキテクチャOPALを提案し、複雑なマニピュレーションタスクでゼロショット性能を向上させつつ推論コストを42%削減した。
著者: Daniel Tcheurekdjian, Joshua Klasmeier, Tom Cooney, Christopher McCann, Tyler Fenstermaker
分類: cs.RO, cs.AI
原文アブストラクト
We present OPAL (Operant Physical Agent with Language), a novel vision-language-action architecture that introduces topological constraints to flow matching for robotic control. To do so, we further introduce topological attention. Our approach models action sequences as topologically-structured representations with non-trivial constraints. Experimental results across 10 complex manipulation tasks demonstrate OPAL's superior performance compared to previous approaches, including Octo, OpenVLA, and ${\pi}$0. Our architecture achieves significant improvements in zero-shot performance without requiring task-specific fine-tuning, while reducing inference computational requirements by 42%. The theoretical guarantees provided by our topological approach result in more coherent long-horizon action sequences. Our results highlight the potential of constraining the search space of learning problems in robotics by deriving from fundamental physical laws, and the possibility of using topological attention to embed causal understanding into transformer architectures.