So You Think You Can Scale Up Autonomous Robot Data Collection?
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.
So You Think You Can Scale Up Autonomous Robot Data Collection? Abstract A long-standing goal in robot learning is to develop methods for robots to acquire new skills autonomously. While reinforcement learning (RL) comes with the promise of enabling autonomous data collection, it remains challenging to scale in the real-world partly due to the significant effort required for environment design and instrumentation, including the need for designing reset functions or accurate success detectors. On the other hand, imitation learning (IL) methods require little to no environment design effort, but
Explore this link on the map →related reading
- State of Robot Learning, December 2025vedder.io
- The Era of Experience Paper.pdfstorage.googleapis.com
- Fully autonomous robots are much closer than you think – Sergey Levinedwarkesh.com
- Generalist - GEN-0 / Embodied Foundation Models That Scale with Physical Interactiongeneralistai.com
- pistar06.pdfpi.website
- A VLA that Learns from Experiencepi.website
- Reward Isn’t Free: Supervising Robot Learning with Language and Video from the Web | SAIL Blogai.stanford.edu
- EgoScaleresearch.nvidia.com
- Q-learning is not yet scalableseohong.me
- Emergence of Human to Robot Transfer in Vision-Language-Action Modelspi.website
- Pantograph: Building a Preschool for Robotspantograph.com
- ALOHA Unleashed: A Simple Recipe for Robot Dexterityaloha-unleashed.github.io