Grounded Decoding：面向具身智能体的基于接地模型引导的文本生成
Grounded Decoding: Guiding Text Generation with Grounded Models for Embodied Agents
AI总结：
针对大语言模型缺乏物理世界接地性、语言条件机器人策略语义理解不足的问题，提出Grounded Decoding方法，通过概率滤波式解码融合两类模型优势，可解决复杂长时程机器人具身任务。
摘要：
大语言模型（LLM）的最新进展已展现出通过自回归模型预训练来学习并利用互联网规模知识的能力。遗憾的是，将此类模型应用于机器人等具身智能体场景仍存在挑战，原因在于它们缺乏物理世界的相关经验，无法解析非语言观测信息，且对机器人可能需要的奖励机制或安全约束一无所知。另一方面，从交互数据中学习的语言条件机器人策略能够提供必要的接地能力，使智能体得以正确适配现实世界，但由于可用于训练的交互数据广度有限，这类策略受限于高层语义理解能力的不足。因此，若要在利用语言模型语义知识的同时使其适配具身场景，我们必须构建既符合语言模型的高概率判定，又能通过环境接地模型验证为可实现的动作序列。我们将此问题构建为类似概率滤波的问题：解码出在语言模型下具有高概率、同时在一组接地模型目标下也具备高概率的序列。我们展示了如何在三个仿真与真实世界领域中获取此类接地模型，且所提出的解码策略能够通过融合两类模型的知识，解决机器人场景中复杂的长时程具身任务。该项目的网站为grounded-decoding.github.io。
英文摘要：
Recent progress in large language models (LLMs) has demonstrated the ability to learn and leverage Internet-scale knowledge through pre-training with autoregressive models. Unfortunately, applying such models to settings with embodied agents, such as robots, is challenging due to their lack of experience with the physical world, inability to parse non-language observations, and ignorance of rewards or safety constraints that robots may require. On the other hand, language-conditioned robotic policies that learn from interaction data can provide the necessary grounding that allows the agent to be correctly situated in the real world, but such policies are limited by the lack of high-level semantic understanding due to the limited breadth of the interaction data available for training them. Thus, if we want to make use of the semantic knowledge in a language model while still situating it in an embodied setting, we must construct an action sequence that is both likely according to the language model and also realizable according to grounded models of the environment. We frame this as a problem similar to probabilistic filtering: decode a sequence that both has high probability under the language model and high probability under a set of grounded model objectives. We demonstrate how such grounded models can be obtained across three simulation and real-world domains, and that the proposed decoding strategy is able to solve complex, long-horizon embodiment tasks in a robotic setting by leveraging the knowledge of both models. The project's website can be found at grounded-decoding.github.io.
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。
