SafeManip is a benchmark applying reusable LTLf templates across eight safety categories to evaluate temporal properties in robotic manipulation on VLA policies.
Responsiblerobotbench: Benchmarking responsible robot manipulation using multi-modal large language models
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TouchSafeBench evaluates VLMs on collision grounding, finding best Macro-F1 below 50% and that explicit depth does not yield reliable robot-body contact inference.
citing papers explorer
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SafeManip: A Property-Driven Benchmark for Temporal Safety Evaluation in Robotic Manipulation
SafeManip is a benchmark applying reusable LTLf templates across eight safety categories to evaluate temporal properties in robotic manipulation on VLA policies.
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Probing Collision Grounding in Vision-Language Models for Safe Human-Robot Collaboration
TouchSafeBench evaluates VLMs on collision grounding, finding best Macro-F1 below 50% and that explicit depth does not yield reliable robot-body contact inference.