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cs.RO updates on arXiv.org

FineCog-Nav: Integrating Fine-grained Cognitive Modules for Zero-shot Multimodal UAV Navigation DENALI: A Dataset Enabling Non-Line-of-Sight Spatial Reasoning with Low-Cost LiDARs SENSE: Stereo OpEN Vocabulary SEmantic Segmentation Continual Hand-Eye Calibration for Open-world Robotic Manipulation PLAF: Pixel-wise Language-Aligned Feature Extraction for Efficient 3D Scene Understanding GaussianFlow SLAM: Monocular Gaussian Splatting SLAM Guided by GaussianFlow GIST: Multimodal Knowledge Extraction and Spatial Grounding via Intelligent Semantic Topology $π_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities R3D: Revisiting 3D Policy Learning Vision-Based Safe Human-Robot Collaboration with Uncertainty Guarantees Benchmarking Classical Coverage Path Planning Heuristics on Irregular Hexagonal Grids for Maritime Coverage Scenarios NEAT-NC: NEAT guided Navigation Cells for Robot Path Planning HRDexDB: A Large-Scale Dataset of Dexterous Human and Robotic Hand Grasps ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints An Intelligent Robotic and Bio-Digestor Framework for Smart Waste Management Efficient closed-form approaches for pose estimation using Sylvester forms World-Value-Action Model: Implicit Planning for Vision-Language-Action Systems A Nonasymptotic Theory of Gain-Dependent Error Dynamics in Behavior Cloning CooperDrive: Enhancing Driving Decisions Through Cooperative Perception SpaceMind: A Modular and Self-Evolving Embodied Vision-Language Agent Framework for Autonomous On-orbit Servicing HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System UMI-3D: Extending Universal Manipulation Interface from Vision-Limited to 3D Spatial Perception Towards Multi-Object-Tracking with Radar on a Fast Moving Vehicle: On the Potential of Processing Radar in the Frequency Domain Beyond Conservative Automated Driving in Multi-Agent Scenarios via Coupled Model Predictive Control and Deep Reinforcement Learning Failure Identification in Imitation Learning Via Statistical and Semantic Filtering A Dynamic-Growing Fuzzy-Neuro Controller, Application to a 3PSP Parallel Robot Vision-Language-Action Jump-Starting for Reinforcement Learning Robotic Agents A Mechanistic Analysis of Sim-and-Real Co-Training in Generative Robot Policies ESCAPE: Episodic Spatial Memory and Adaptive Execution Policy for Long-Horizon Mobile Manipulation Evolvable Embodied Agent for Robotic Manipulation via Long Short-Term Reflection and Optimization
Lessons from the Cruise Robotaxi Pedestrian Dragging Mishap
Philip Koopman · 2024-06-08 · via cs.RO updates on arXiv.org

A robotaxi dragged a pedestrian 20 feet down a San Francisco street on the evening of October 2, 2023, coming to rest with its rear wheel on that woman's legs. The mishap was complex, involving a first impact by a different, human-driven vehicle. The following weeks saw Cruise stand down its road operations amid allegations of withholding crucial mishap information from regulators. The pedestrian has survived her severe injuries, but the robotaxi industry is still wrestling with the aftermath. Key observations include that the robotaxi had multiple possible ways available to avoid initial impact with the pedestrian. Limitations to the computer driver's programming prevented it from recognizing a pedestrian was about to be hit in an adjacent lane, caused the robotaxi to lose tracking of and then in essence forget a pedestrian who was hit by an adjacent vehicle, and forget that the robotaxi had just run over a presumed pedestrian when beginning a subsequent repositioning maneuver. The computer driver was unable to detect the pedestrian being dragged even though her legs were partially in view of a robotaxi camera. Moreover, more conservative operational approaches could have avoided the dragging portion of the mishap entirely, such as waiting for remote confirmation before moving after a crash with a pedestrian, or operating the still-developing robotaxi technology with an in-vehicle safety driver rather than prioritizing driver-out deployment.