IROS

Robust Slip Detection and Material Classification via Spatiotemporal Transformers on a Uniformly-Illuminated Visuo-Tactile Sensor

基于均匀照明视觉-触觉传感器的时空Transformer的鲁棒滑移检测与材料分类

Ziyang Ma, Yuhao Sun, Zichen Ai, Xiangyang Ji, Bin Fang

机构 * School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院） 纠错

AI总结 本研究开发了带均匀RGB照明的视觉-触觉传感器与统一感知框架，采用双头部TimeSformer网络和ResNet-50主干，实现高精度滑移检测与物体分类，为机器人操作提供多模态感知基线。

Comments Accepted at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2026. 8 pages, 10 figures

Effort-Based Criticality Metrics for Evaluating 3D Perception Errors in Autonomous Driving

基于努力的临界度量指标用于评估自动驾驶中的3D感知误差

Sharang Kaul, Simon Bultmann, Mario Berk, Abhinav Valada

机构 * CARIAD SE ； University of Freiburg（弗莱堡大学） 纠错

AI总结 本文提出基于努力的临界度量指标，通过FSR、MDR和LEA量化自动驾驶中3D感知误差的严重性，揭示非关键性误差占比高，验证新指标能捕捉安全关键信息。

Comments Accepted at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2026

Adaptive Multi-Mode Out-of-Distribution Detection for Trajectory Prediction in Autonomous Vehicles

动态感知：面向自动驾驶轨迹预测的自适应多模式异常检测

Tongfei Guo, Lili Su

机构 * Department of Electrical and Computer Engineering, Northeastern University（东北大学电气与计算机工程系） 纠错

AI总结 本文提出一种自适应多模式异常检测框架，通过建模误差模式提升轨迹预测的鲁棒性，在复杂驾驶环境中实现更高效的异常检测。

Comments Accepted to the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)