Deep reinforcement learning achieves real-time cooling of single-atom motion with a 388 microsecond time constant using cavity feedback, outperforming a linear differentiator controller.
Sim-to-real transfer in deep reinforce- ment learning for robotics: A survey
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A world-action model trained on ~800 synthetic demonstrations per task achieves 35% zero-shot success on real-robot manipulation tasks.
EmbodiedGovBench is a new benchmark framework that measures embodied agent systems on seven governance dimensions including policy adherence, recovery success, and upgrade safety.
Object-centric residual RL trained in simulation with pose noise and dropout raises real Franka robot VLA success from 42% to 76% zero-shot across five tasks, with improved data reusable for base model retraining.
Real-robot trials with OpenVLA on a UR5e arm show consistent offline-to-closed-loop gaps driven by action semantics, coordinate conventions, temporal alignment, image preprocessing, and dataset quality rather than model capacity.
citing papers explorer
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Deep Reinforcement Learning for Individual Atomic Control and Cooling
Deep reinforcement learning achieves real-time cooling of single-atom motion with a 388 microsecond time constant using cavity feedback, outperforming a linear differentiator controller.
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Efficient Sim-to-Real Transfer of World-Action Models from Synthetic Priors
A world-action model trained on ~800 synthetic demonstrations per task achieves 35% zero-shot success on real-robot manipulation tasks.
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EmbodiedGovBench: A Benchmark for Governance, Recovery, and Upgrade Safety in Embodied Agent Systems
EmbodiedGovBench is a new benchmark framework that measures embodied agent systems on seven governance dimensions including policy adherence, recovery success, and upgrade safety.
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Object-Centric Residual RL for Zero-Shot Sim-to-Real VLA Enhancement
Object-centric residual RL trained in simulation with pose noise and dropout raises real Franka robot VLA success from 42% to 76% zero-shot across five tasks, with improved data reusable for base model retraining.
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Vision-Language-Action Models: Experimental Insights from a Real-World UR5 Platform
Real-robot trials with OpenVLA on a UR5e arm show consistent offline-to-closed-loop gaps driven by action semantics, coordinate conventions, temporal alignment, image preprocessing, and dataset quality rather than model capacity.