arXiv:2606.02194v1 Announce Type: new Abstract: Distilling expert demonstration data into large generative models using behavioral cloning is a scalable approach to learning capable policies for robotic control, particularly for dexterous manipulation. Reinforcement learning (RL) can be used as a means to finetune these policies further using additional experience. An open question is whether RL is more sample-efficient than collecting more human demonstrations. Prior work has finetuned large pretrained policies in a scalable fashion by applying RL to a smaller residual policy that corrects th
The continuous advancements in large generative models and the increasing push for sophisticated robotic control necessitate more efficient and scalable policy learning techniques, moving beyond pure behavioral cloning.
Improving sample efficiency in learning for large behavior models through methods like reinforcement learning is crucial for accelerating the development and deployment of advanced AI applications, particularly in robotics.
This research outlines a method to significantly enhance the performance and data efficiency of large behavior models for robotic control by integrating learned rewards with off-policy improvement, potentially reducing the reliance on extensive human demonstrations.
- · AI research labs
- · Robotics companies
- · Developers of large behavior models
- · Manufacturing sector
- · Companies relying solely on behavioral cloning
- · Fields requiring massive human demonstration datasets
More capable and robust AI policies for robotic control can be developed with less data, reducing development costs and time.
This efficiency gain could accelerate the readiness and broader adoption of AI-driven automation in real-world physical tasks, including dexterous manipulation.
Reduced data dependency might democratize access to advanced robotics for entities with fewer resources for data collection, potentially impacting competitive landscapes.
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Read at arXiv cs.LG