LWD is a fleet-scale offline-to-online RL framework that continually improves pretrained VLA policies using autonomous rollouts and human interventions, reaching 95% average success on real-world manipulation tasks.
Deep rl at scale: Sorting waste in office buildings with a fleet of mobile manipulators
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DF-ExpEnse improves sample efficiency in finetuning diffusion-based robotic policies by filtering diffusion-generated actions with critic ensembles and enabling fleet-level collaboration.
citing papers explorer
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Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies
LWD is a fleet-scale offline-to-online RL framework that continually improves pretrained VLA policies using autonomous rollouts and human interventions, reaching 95% average success on real-world manipulation tasks.
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DF-ExpEnse: Diffusion Filtered Exploration for Sample Efficient Finetuning
DF-ExpEnse improves sample efficiency in finetuning diffusion-based robotic policies by filtering diffusion-generated actions with critic ensembles and enabling fleet-level collaboration.