SoGuDiff：用于可操控、规范接地机器人导航的社会引导扩散

中文摘要 AI 辅助

除了避免碰撞之外，具有社会能力的机器人导航还需要遵守随情境、文化和部署要求而变化的隐性社会规范。许多传统导航策略通过针对固定奖励函数的强化学习或模仿人类演示来学习单一规范行为，在运行时没有提供调整该行为的接口。我们提出了一种基于扩散的导航框架，其社会行为可在部署时进行调谐：可指定期望的风格，例如机器人经过行人的距离、让行的一侧或对群体的避让程度，规划器会相应调整。连续风格轴可独立遵循或组合使用，跨越行为空间而非离散的、基于原语的规范。可行性投影层将学习到的社会行为与运动学可行性和碰撞避免分离。单轴扫描展示了严格优于所评估的固定行为基线配置的权衡曲线，风格差异在真实世界演示中得到了复现。

英文摘要

Beyond collision avoidance, socially competent robot navigation requires adherence to implicit social conventions that vary across contexts, cultures, and deployment requirements. Many conventional navigation policies learn a single normative behavior, either through reinforcement learning against a fixed reward function or imitation of human demonstrations, exposing no interface for adjusting that conduct at runtime. We present a diffusion-based navigation framework whose social behavior can be tuned at deployment: a desired style is specified, such as how closely the robot passes, which side it yields to, or how much it defers to groups, and the planner adapts accordingly. Continuous style axes can be followed independently or composed, spanning a behavioral space rather than discrete, primitive-based specifications. A feasibility projection layer separates learned social behavior from kinematic feasibility and collision avoidance. A single-axis sweep illustrates a tradeoff curve that strictly dominates the evaluated fixed-behavior baseline configurations, and stylistic differences are replicated in real-world demonstrations.

发表机构

• University of Waterloo（滑铁卢大学）
• McMaster University（麦克马斯特大学）

机构由 AI 辅助整理，请以论文原文为准。