外观
论文arXiv 2609.343561 个月前
预测性语义安全：从视觉物理推理到安全关键控制
原文链接：Predictive Semantic Safety: From Visual Physical Reasoning to Safety-Critical Control（arXiv 2609.34356 · 发布日期：2026-09-28）
Predictive Semantic Safety: From Visual Physical Reasoning to Safety-Critical Control
PSS用VLM预测未来物理事件或位移，并结合备份安全过滤，在MuJoCo的Unitree Go1场景中验证。
核心要点
- 当前几何环境可能无法显现物理交互导致的未来危险。
- PSS将VLM视觉物理推理与基于备份的安全过滤连接起来。
- 通过运动模型、分裂共形预测和形状边界生成预测占用。
- 在MuJoCo实验中，安全回合率为99.3%，高于仅用当前几何的基线43.3%。
验证范围与局限：验证限于MuJoCo中Unitree Go1的坠落固定物、撞击导致支撑丧失和接触传播场景。
英文摘要 (Abstract)
Physical interactions can create future hazards that are not apparent from the robot's current geometric surroundings. We present a framework termed Predictive Semantic Safety (PSS), which connects visual physical reasoning to backup-based safety filtering. A vision-language model (VLM) predicts physical events and their timing or directly predicts object displacements. An explicit motion model converts event hypotheses into object trajectories. Split conformal prediction calibrates position errors jointly across specified objects, observation times, and future times; geometric shape bounds convert the resulting position regions into predicted object occupancy. PSS evaluates a prescribed backup maneuver against this occupancy and derives input-affine constraints for minimally modifying the nominal input while preserving backup feasibility under the robot dynamics and input limits. MuJoCo experiments with a Unitree Go1 consider falling fixtures, impact-driven support loss, and contact propagation. PSS achieves a safe episode rate of 99.3%, compared with 43.3% for a Backup Control Barrier Function baseline that only uses current obstacle geometry.
作者：Taekyung Kim, Salem Fradi, Yanning Dai, Mateusz Ostaszewski, Jürgen Schmidhuber
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