SafetyExpert
Probabilistic Ship Trajectory Forecasting using Transformer Models on AIS Data
Anticipation of vessel trajectories is an essential task when navigating at sea. This applies to both human navigators and autonomous surface vessels.
Research
Papers
New robotics and physical-AI papers, with what each one means for the industry.
SafetyExpert
EndoRSI: Physical-Evidence-Driven Recursive Improvement of Endoscopic Robot Policies
Artificial intelligence is beginning to automate scientific discovery, yet physical robot-policy research still relies on scientists and engineers to interpret experimental failures and decide how controllers should be revised. We present EndoRSI (Endoscopic Recursive Self-Improvement), a human-governed, self-improving robotic research system in which the embedded EndoForge coding agent iteratively develops and refines endoscopic robot policies using evidence from physical experiments.
SafetyExpert
A Local Agentic AI Framework to Generate Edge Case Scenarios for Safety Assurance
Generating edge case scenarios is crucial for training, testing and safety assuring autonomous systems. This identifies hazardous failures to increase system reliability and robustness, ensures the system can still function safely on the boundaries of its operational design domain and learns the expected system behaviour for these edge cases.
SafetyExpert
Neuro-Symbolic Predicate Learning for Semantic Safe Robot Control
This preprint introduces NEUPRO, a neuro-symbolic safety system that learns reusable visual safety predicates from human-written symbolic rules through a differentiable graph reasoner. On the REASON real-robot benchmark it reaches 92% mean safety-classification accuracy across seven tasks versus 55% for Qwen3.5 and 44% for DeepSeek-VL2, and it can explain violations in first-order logic. The result matters because it offers a path to interpretable, transferable safety constraints instead of opaque per-task cost functions.
SafetyExpert
ExceptionDrive: A Planning-Oriented Counterfactual Corner-Case Benchmark for Autonomous Driving
Average performance on routine driving benchmarks does not establish planner reliability under rare, safety-critical hazards. We proposed ExceptionDrive, a counterfactual planning benchmark that uses VLM-assisted screening, localized multi-view editing, and quality auditing to insert hazards into real nuScenes scenes while preserving their context.
Topic pages
Papers come from arXiv robotics feeds. Where our summary is not written yet, you see the opening lines of the abstract.