VLA-RL applies online RL to pretrained VLAs, yielding a 4.5% gain over strong baselines on 40 LIBERO manipulation tasks and matching commercial models like π₀-FAST.
Causal policy gradient for whole-body mobile manipulation
4 Pith papers cite this work. Polarity classification is still indexing.
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A low-cost whole-body teleoperation system enables effective imitation learning for complex bimanual mobile manipulation by co-training on mobile and static demonstration datasets.
DynaMOMA uses an anchor-based diffusion predictor for temporally consistent grasp trajectories and feeds encoded features to an anticipation-guided whole-body RL policy, reporting strong simulation performance and real-world generalizability for dynamic mobile grasping.
A four-stage RL system with teacher-student distillation and online constrained adaptation enables humanoid robots to achieve robust ball-kicking accuracy under noisy perception in simulation and on physical hardware.
citing papers explorer
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VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning
VLA-RL applies online RL to pretrained VLAs, yielding a 4.5% gain over strong baselines on 40 LIBERO manipulation tasks and matching commercial models like π₀-FAST.
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Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation
A low-cost whole-body teleoperation system enables effective imitation learning for complex bimanual mobile manipulation by co-training on mobile and static demonstration datasets.
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DynaMOMA: Instantaneous Prediction of Grasp Poses for Mobile Manipulation of Dynamic Objects
DynaMOMA uses an anchor-based diffusion predictor for temporally consistent grasp trajectories and feeds encoded features to an anticipation-guided whole-body RL policy, reporting strong simulation performance and real-world generalizability for dynamic mobile grasping.
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Learning Agile Striker Skills for Humanoid Soccer Robots from Noisy Sensory Input
A four-stage RL system with teacher-student distillation and online constrained adaptation enables humanoid robots to achieve robust ball-kicking accuracy under noisy perception in simulation and on physical hardware.