NeSyC：面向开放域复杂具身任务的神经符号持续学习器
NeSyC: A Neuro-symbolic Continual Learner For Complex Embodied Tasks In Open Domains
AI总结：
针对具身智能体在开放域中因经验受限而难以泛化知识的问题，提出神经符号持续学习框架NeSyC，结合LLM与符号工具迭代生成并验证假设，有效解决复杂具身任务。
摘要：
我们探索神经符号方法以泛化可操作知识，使具身智能体能在开放域环境中更有效地处理复杂任务。具身智能体的一个关键挑战是在多样环境和情境间泛化知识，因为有限的经验常将其局限于先验知识。为解决此问题，我们提出一种新框架NeSyC，这是一种神经符号持续学习器，它通过结合使用大型语言模型（LLMs）和符号工具，从有限经验中持续形成并验证知识，从而模拟假设演绎模型。具体而言，我们在NeSyC中设计了一种对比泛化性改进方案，利用LLMs迭代生成假设，并通过符号工具进行对比验证。该方案增强了对可执行动作的合理性证明，同时最小化对不可执行动作的推断。此外，我们引入了一种基于内存的监控方案，能高效检测动作错误并触发跨域的知识精炼过程。在多种具身任务基准上进行的实验——包括ALFWorld、VirtualHome、Minecraft、RLBench及一个真实世界机器人场景——表明NeSyC在解决一系列开放域环境中的复杂具身任务方面高度有效。
英文摘要：
We explore neuro-symbolic approaches to generalize actionable knowledge, enabling embodied agents to tackle complex tasks more effectively in open-domain environments. A key challenge for embodied agents is the generalization of knowledge across diverse environments and situations, as limited experiences often confine them to their prior knowledge. To address this issue, we introduce a novel framework, NeSyC, a neuro-symbolic continual learner that emulates the hypothetico-deductive model by continually formulating and validating knowledge from limited experiences through the combined use of Large Language Models (LLMs) and symbolic tools. Specifically, we devise a contrastive generality improvement scheme within NeSyC, which iteratively generates hypotheses using LLMs and conducts contrastive validation via symbolic tools. This scheme reinforces the justification for admissible actions while minimizing the inference of inadmissible ones. Additionally, we incorporate a memory-based monitoring scheme that efficiently detects action errors and triggers the knowledge refinement process across domains. Experiments conducted on diverse embodied task benchmarks-including ALFWorld, VirtualHome, Minecraft, RLBench, and a real-world robotic scenario-demonstrate that NeSyC is highly effective in solving complex embodied tasks across a range of open-domain environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
