GUIDES: 利用教师蒸馏嵌入进行预训练机器人策略增强
GUIDES: Guidance Using Instructor-Distilled Embeddings for Pre-trained Robot Policy Enhancement
AI总结：
GUIDES通过教师蒸馏嵌入提升预训练机器人策略，实现语义增强与高效升级
摘要：
预训练的机器人策略是许多经过验证的机器人系统的基础，它们封装了大量具身知识。然而，它们通常缺乏基础模型所具有的语义意识特征，并且在许多情况下无法完全替换它们，因为成本高且会损失积累的知识。为了解决这一差距，我们引入了GUIDES，一个轻量级框架，通过基础模型的语义指导来增强预训练策略，而无需进行架构重设计。GUIDES利用微调的视觉语言模型（Instructor）生成上下文指令，这些指令通过辅助模块编码为指导嵌入。这些嵌入被注入到策略的潜在空间中，使遗留模型能够通过简短的针对性微调适应这种新的语义输入。为了推理时的鲁棒性，基于大型语言模型的Reflector监控Instructor的置信度，当置信度较低时，会启动一个推理循环，分析执行历史，检索相关示例，并增强VLM的上下文以优化后续动作。在RoboCasa模拟环境中的广泛验证，涵盖了多种策略架构，显示了任务成功率的一致性和显著提升。在UR5机器人上的实际部署进一步表明，GUIDES增强了抓取等关键子任务的运动精度。总体而言，GUIDES提供了一种实用且资源高效的升级途径，而不是完全替换经过验证的机器人策略。
英文摘要：
Pre-trained robot policies serve as the foundation of many validated robotic systems, which encapsulate extensive embodied knowledge. However, they often lack the semantic awareness characteristic of foundation models, and replacing them entirely is impractical in many situations due to high costs and the loss of accumulated knowledge. To address this gap, we introduce GUIDES, a lightweight framework that augments pre-trained policies with semantic guidance from foundation models without requiring architectural redesign. GUIDES employs a fine-tuned vision-language model (Instructor) to generate contextual instructions, which are encoded by an auxiliary module into guidance embeddings. These embeddings are injected into the policy's latent space, allowing the legacy model to adapt to this new semantic input through brief, targeted fine-tuning. For inference-time robustness, a large language model-based Reflector monitors the Instructor's confidence and, when confidence is low, initiates a reasoning loop that analyzes execution history, retrieves relevant examples, and augments the VLM's context to refine subsequent actions. Extensive validation in the RoboCasa simulation environment across diverse policy architectures shows consistent and substantial improvements in task success rates. Real-world deployment on a UR5 robot further demonstrates that GUIDES enhances motion precision for critical sub-tasks such as grasping. Overall, GUIDES offers a practical and resource-efficient pathway to upgrade, rather than replace, validated robot policies.
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。
