CrossSafe：迈向跨具身潜在安全滤波器

中文摘要 AI 辅助

跨具身学习已表明，单一模型（如视觉-语言-动作（VLA）模型）能够学习可应用于异构机器人以完成各种任务的状态表征和操作技能。我们假设安全执行也是如此。满足安全约束所需的推理，例如检测障碍物、识别其应被避开以及选择安全的抽象动作，在机器人之间很大程度上是共享的。不同具身之间的差异在于抽象安全动作如何实现：形态、运动学和动力学决定了哪些动作是安全且可行的。因此，同一动作对一台机器人可能是安全的，而对另一台机器人则可能不安全。这对于在共同末端执行器动作空间中运行、而未显式捕获安全性如何依赖于机器人形态和运动学的通用操作策略尤为重要。我们提出具身条件化安全滤波，其中基于Hamilton-Jacobi可达性的值函数及其对应的安全最大化策略在机器人之间共享。利用机器人及其环境的形态感知潜在表征，我们直接在潜在空间中进行Hamilton-Jacobi可达性分析，使得学习到的安全概念能够跨具身泛化，同时保持对每个机器人形态和运动学的显式条件化。我们在五个双臂机器人具身和五个具有全身避碰约束的操作任务上评估了我们的方法。结果表明，一个在五个操作任务和四个具身上联合训练的单一策略，对保留的具身展现出零样本泛化能力，降低了名义策略的碰撞率。结果还表明，使用更多具身进行训练可提高泛化能力。

英文摘要

Cross-embodiment learning has shown that a single model, such as a vision-language-action (VLA) model, can learn state representations and manipulation skills that can be applied across heterogeneous robots to accomplish various tasks. We hypothesize that the same holds for safety enforcement. The reasoning required to satisfy a safety constraint, such as detecting an obstacle, recognizing that it should be avoided, and selecting a safe abstract action, is largely shared across robots. What differs across embodiments is how the abstract safe action is realized: morphology, kinematics, and dynamics determine which actions are safe and feasible. Consequently, the same action can be safe for one robot and unsafe for another. This is especially important for generalist manipulation policies that operate in a common end-effector action space without explicitly capturing how safety depends on the robot's morphology and kinematics. We propose embodiment-conditioned safety filtering, in which a Hamilton-Jacobi reachability-based value function and its corresponding safety-maximizing policy are shared across robots. Using a morphology-aware latent representation of the robot and its environment, we perform Hamilton-Jacobi reachability analysis directly in latent space so that the learned safety concepts can generalize across embodiments while remaining explicitly conditioned on each robot's morphology and kinematics. We evaluate our approach across five bimanual robot embodiments and five manipulation tasks with whole-body collision-avoidance constraints. Our results show that a single policy, jointly trained across five manipulation tasks and four embodiments, exhibits zero-shot generalization to a held-out embodiment, reducing the nominal policy's collision rate. They also show that training using more embodiments improves generalization.

发表机构

• Washington University in St. Louis（圣路易斯华盛顿大学）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

Updated acknowledgements section