面向未知物理环境地面机器人的认知基础型端侧运行时学习

中文摘要 AI 辅助

本文提出了CogRun框架，该框架可让安全关键型地面机器人在未知物理环境中完全在边缘AI设备上执行基于认知的运行时学习，无需先验地图或感知知识。CogRun包含三个组件：学习智能体（Learning-Agent）、理性智能体（Rational-Agent）和协调器（Coordinator）。学习智能体的认知神经学习架构具有创新性，包含专用回放缓冲区、认知驱动的经验采样，以及结合了基于实例的学习（IBL）的演员-评论家强化学习（RL）的安全感知动作融合。理性智能体是一个非学习模块，专门处理安全关键功能以补充学习智能体，而协调器则管理两个智能体之间的交互，以促进安全高效的运行时学习。CogRun在边缘AI设备上的完整自主栈（即感知、学习和控制）消除了对无线通信的依赖，使其在连接受限或无连接的挑战性环境中具有更广泛的应用。在真实野外森林中的四足机器人和模拟野外森林中的越野自动驾驶车辆上进行的实验表明，CogRun可实现安全高效的运行时学习，使机器人能够安全持续地与物理世界交互，以提升复杂未知环境中的任务性能。

英文摘要

This paper presents \ul{CogRun}, a framework that enables safety-critical ground robots to perform cognitively-grounded runtime learning entirely on edge-AI devices in unknown physical environments, without prior maps or perceptual knowledge. CogRun consists of three components: a Learning-Agent, a Rational-Agent, and a Coordinator. The Learning-Agent is novel in cognitive-neural learning architecture, which featurs dedicated replay buffers, cognition-driven experience sampling, and a safety-aware action blending of actor-critic reinforcement learning (RL) with instance-based learning (IBL). The Rational-Agent is a non-learning module that complements the Learning-Agent by exclusively handling safety-critical functions, while the Coordinator manages interactions between the two agents to promote safe and efficient runtime learning. CogRun's full autonomy stack (i.e., perception, learning, and control) on edge-AI devices eliminates dependence on wireless communications, enabling broader applications in challenging environments with limited or no connectivity. Experiments on a quadruped robot in real-world wild forests and on an off-road autonomous vehicle in a simulated wild forest demonstrate that CogRun enables safe and efficient runtime learning, allowing robots to safely and continuously interact with the physical world for enhancing task performance in complex, unknown environments.

发表机构

• Wayne State University（韦恩州立大学）
• Carnegie Mellon University（卡内基梅隆大学）

机构由 AI 辅助整理，请以论文原文为准。