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Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons
arXiv – CS AI|Anthony Liang, Yigit Korkmaz, Jiahui Zhang, Minyoung Hwang, Abrar Anwar, Sidhant Kaushik, Aditya Shah, Alex S. Huang, Luke Zettlemoyer, Dieter Fox, Yu Xiang, Anqi Li, Andreea Bobu, Abhishek Gupta, Stephen Tu, Erdem Biyik, Jesse Zhang||3 views
🤖AI Summary
Researchers introduce Robometer, a new framework for training robot reward models that combines progress tracking with trajectory comparisons to better learn from failed attempts. The system is trained on RBM-1M, a dataset of over one million robot trajectories including failures, and shows improved performance across diverse robotics applications.
Key Takeaways
- →Robometer uses dual supervision combining frame-level progress tracking with trajectory comparison preferences.
- →The framework addresses scalability issues in robot training by effectively learning from failed and suboptimal trajectories.
- →RBM-1M dataset contains over one million trajectories spanning diverse robot embodiments and tasks.
- →The system shows improved generalization compared to previous reward modeling methods.
- →Robometer demonstrates better performance across various downstream robotics applications in real-world evaluations.
#robotics#reward-modeling#machine-learning#ai-training#trajectory-comparison#robot-learning#dataset#research
Read Original →via arXiv – CS AI
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