RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
This is experimental HTML to improve accessibility. We invite you to report rendering errors. Use Alt+Y to toggle on accessible reporting links and Alt+Shift+Y to toggle off. Learn more about this project and help improve conversions.
\reportnumber \correspondingauthor Jianlan Luo( jianlanluo@berkeley.edu ), Charles Xu( xuc@berkeley.edu ) RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning Charles Xu Qiyang Li Department of EECS, UC Berkeley Jianlan Luo Sergey Levine Department of EECS, UC Berkeley Abstract Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, their performance heavily depends on the quality of their training data. In this work, we propose Reinforcement Learning Dis
related reading
- State of Robot Learning, December 2025vedder.io
- Precise Manipulation with Efficient Online RLpi.website
- A Steerable Model with Emergent Capabilitiespi.website
- A VLA with Open-World Generalizationpi.website
- Emergence of Human to Robot Transfer in Vision-Language-Action Modelspi.website
- How Claude Performs on Robotics Tasks \ Anthropicanthropic.com
- Generalist - GEN-0 / Embodied Foundation Models That Scale with Physical Interactiongeneralistai.com
- pistar06.pdfpi.website
- Causal Video Models Are Data-Efficient Robot Policy Learners | Rhoda AIrhoda.ai
- $π_0$: A Vision-Language-Action Flow Model for General Robot Controlalphaxiv.org
- Beyond Human Demonstrations: Diffusion-Based Reinforcement Learning to Generate Data for VLA Trainingalphaxiv.org
- SimpleVLA-RL: Scaling VLA Training via Reinforcement Learningalphaxiv.org