SERNF: Sample-Efficient Real-World Dexterous Policy Fine-Tuning via Action-Chunked Critics and Normalizing Flows
arXiv:2602.09580v4 Announce Type: replace-cross Abstract: Real-world fine-tuning of dexterous manipulation policies remains challenging due to limited real-world interaction budgets and highly multimodal action distributions. Diffusion-based policies, while expressive, do not permit conservative likelihood-based updates during fine-tuning because action probabilities are intractable. In contrast, conventional Gaussian policies collapse under multimodality, particularly when actions are executed in chunks, and standard per-step critics fail to align with chunked execution, leading to poor credi
The continuous drive towards making AI systems more capable in real-world physical environments, particularly for complex dexterous tasks, pushes research into optimizing policy fine-tuning with limited data.
Improving sample efficiency and the ability to fine-tune dexterous manipulation policies in the real world is crucial for the deployment of advanced robotics in various industries.
This research introduces methodologies that enable more robust and data-efficient fine-tuning of robotic manipulation policies, overcoming limitations of previous approaches like diffusion models and conventional Gaussian policies.
- · Robotics companies
- · Automation industries
- · AI researchers in reinforcement learning
- · Logistics and manufacturing sectors
- · Tasks requiring extensive manual dexterous labor
- · Inefficient robot training methodologies
Robots will become more proficient and adaptable in complex, real-world manipulation tasks.
This improved dexterity could accelerate the adoption of humanoid robots and advanced robotic arms in unstructured environments.
Increased robotic autonomy and capability in diverse settings may lead to significant shifts in labor markets and supply chain automation.
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Read at arXiv cs.LG