# CrossSafe：迈向跨具身潜空间安全过滤器

> 原文链接：https://robotics.hellobike.com/papers/2026-09-24-2609-28984
> 频道：papers | 更新日期：2026-09-24 | 标签：arxiv, cs.RO
> 核心摘要：CrossSafe用形态感知潜表示在潜空间进行Hamilton-Jacobi可达性安全过滤，并在五种双臂形态和五个操作任务上验证。

# CrossSafe：迈向跨具身潜空间安全过滤器

::: v-pre
> 原文链接：[CrossSafe: Towards Cross-Embodiment Latent Safety Filters](https://arxiv.org/abs/2609.28984)（arXiv 2609.28984 · 发布日期：2026-09-24）

**CrossSafe: Towards Cross-Embodiment Latent Safety Filters**

CrossSafe用形态感知潜表示在潜空间进行Hamilton-Jacobi可达性安全过滤，并在五种双臂形态和五个操作任务上验证。

## 核心要点

- 通用操作策略常用统一末端动作空间，难以显式刻画形态和运动学相关安全性。
- 方法共享Hamilton-Jacobi可达性价值函数与安全最大化策略，并以形态感知潜表示条件化。
- 在五个任务、四种形态联合训练后，单一策略可零样本泛化到保留形态并降低碰撞率。
- 结果还显示，训练中纳入更多机器人形态可改善跨形态泛化。

**验证范围与局限**：验证范围为五种双臂机器人形态和五个带全身避碰约束的操作任务。

## 英文摘要 (Abstract)

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.

作者：Ihab Tabbara, Yuxuan Yang, Hussein Sibai
:::

## 原始参考来源 (Sources)

- [arXiv 2609.28984：CrossSafe: Towards Cross-Embodiment Latent Safety Filters](https://arxiv.org/abs/2609.28984)