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.
Safe learning for contact-rich robot tasks: A survey from classical learning-based methods to safe foundation models
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CMP projects actions onto a learned competence manifold using a frame-wise safety scheme and isomorphic latent space to achieve up to 10x better survival in out-of-distribution scenarios with under 10% tracking loss.
Proposes a Red Team-Blue Team adversarial gamification architecture to generate synthetic hazardous scenarios for learning robot safety policies.
A hierarchical tactile-aware policy combines human-demonstration training for contact cue prediction with sim-to-real reinforcement learning to improve quadrupedal loco-manipulation performance by 28.54% over vision baselines on contact-rich 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.
citing papers explorer
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DreamAvoid: Critical-Phase Test-Time Dreaming to Avoid Failures in VLA Policies
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.
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CMP: Robust Whole-Body Tracking for Loco-Manipulation via Competence Manifold Projection
CMP projects actions onto a learned competence manifold using a frame-wise safety scheme and isomorphic latent space to achieve up to 10x better survival in out-of-distribution scenarios with under 10% tracking loss.
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Learning of Robot Safety Policies via Adversarial Synthetic Scenarios
Proposes a Red Team-Blue Team adversarial gamification architecture to generate synthetic hazardous scenarios for learning robot safety policies.
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Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies
A hierarchical tactile-aware policy combines human-demonstration training for contact cue prediction with sim-to-real reinforcement learning to improve quadrupedal loco-manipulation performance by 28.54% over vision baselines on contact-rich tasks.
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Rule-based High-Level Coaching for Goal-Conditioned Reinforcement Learning in Search-and-Rescue UAV Missions Under Limited-Simulation Training
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.