用拓扑约束提升机器人动作序列的因果理解与效率
OPAL: Encoding Causal Understanding of Physical Systems for Robot Learning
- 引入拓扑注意力，将动作序列建模为有约束的结构化表示
- 零样本性能超越基线模型，推理计算量降低42%
- 适合研究具身智能与物理规律驱动的机器人学习
我们提出OPAL（带语言的操作物理智能体），一种新型视觉-语言-动作架构，通过在流匹配中引入拓扑约束来实现机器人控制。为此，我们进一步提出拓扑注意力机制，将动作序列建模为具有非平凡约束的拓扑结构表示。在10个复杂操作任务上的实验表明，OPAL性能优于Octo、OpenVLA和$π$0等先前方法。该架构在无需任务特定微调的情况下显著提升零样本性能，同时将推理计算需求降低42%。理论保证来自拓扑方法，使长时序动作序列更连贯。结果表明，通过源自基本物理定律约束学习问题的搜索空间具有潜力，且拓扑注意力可用于在Transformer架构中嵌入因果理解。
原文摘要 · Abstract (English)
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 $π$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.
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