SafeManip is a benchmark applying reusable LTLf templates across eight safety categories to evaluate temporal properties in robotic manipulation on VLA 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.
AutoSERL achieves strong performance on six real-world robot manipulation tasks using RL guided by a single demonstration via sliding-window intervention, safety recovery, and automatic termination.
Fine-tuning VLMs with pairwise progress supervision from policy rollouts improves fine-grained failure detection and boosts robot manipulation success by 11% real-world and 5.9% in simulation.
UniIntervene uses future-conditioned action-value estimation and a temporal value-risk critic to trigger memory-based recovery interventions, reporting 8.6% higher success rates and 57% fewer human interventions than prior HiL-RL methods on real manipulation tasks.
AEGIS uses activation probes for early-warning detection of high-risk steps in weak policies and selectively escalates to stronger policies, recovering 10.1% of lost trajectories on LIBERO-Spatial while activating the strong policy on only 38% of steps.
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.
FAR combines failure-contrastive preference adaptation with action perturbations for test-time recovery and continual policy improvement, reporting 17.6% and 11.7% success gains over diffusion policies in simulation and real-world manipulation tasks.
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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