SafeManip is a new benchmark that applies LTLf monitors to assess temporal safety properties across eight categories in robotic manipulation, demonstrating that task success frequently fails to ensure safe execution in vision-language-action policies.
Failure-Aware RL: Reliable Offline- to-Online Reinforcement Learning with Self-Recovery for Real-World Manipulation
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DreamAvoid uses a Dream Trigger, Action Proposer, and Dream Evaluator trained on success/failure/boundary data to let VLA policies avoid critical-phase failures via test-time future dreaming.
ZPRL adapts frozen flow-matching imitation policies via RL perturbations on a task-relevant bottleneck latent, yielding 33.7% higher average success on four real-world manipulation tasks than action-residual baselines.
A learning-augmented robotic system automated deformable cable insertion and soldering on a live electric-motor production line for 5 hours 10 minutes, producing 108 motors at 99.4% pass rate with under 20 minutes of real-world data per task and no physical fencing.
Rule-based high-level guidance combined with goal-conditioned reinforcement learning enables safer and more efficient online adaptation for UAV search-and-rescue tasks under limited simulation training.
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SafeManip: A Property-Driven Benchmark for Temporal Safety Evaluation in Robotic Manipulation
SafeManip is a new benchmark that applies LTLf monitors to assess temporal safety properties across eight categories in robotic manipulation, demonstrating that task success frequently fails to ensure safe execution in vision-language-action policies.