Summary of Privileged Sensing Scaffolds Reinforcement Learning, by Edward S. Hu et al.
Privileged Sensing Scaffolds Reinforcement Learning
by Edward S. Hu, James Springer, Oleh Rybkin, Dinesh Jayaraman
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A proposed reinforcement learning approach called Scaffolder exploits privileged sensing in critics, world models, and other auxiliary components to improve the target policy. The method is designed for training artificial agents that need access to sensory information during training but not at test time. For example, a robot arm may use a low-cost camera during training and a more advanced motion capture rig only during training. The Scaffolder approach outperforms prior baselines in a range of simulated robotic tasks, including blind hurdlers, occluded vision, and tactile sensing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial agents can learn to tie their shoelaces by using sensory information, but once they master this skill, they can do it from touch alone. This is called “sensory scaffolding.” Researchers propose a new approach that uses privileged sensing during training to improve the agent’s performance. The method is tested on ten simulated robotic tasks, including using cameras and sensors to overcome visual occlusions and train robot hands. The results show that the proposed method outperforms previous approaches. |
Keywords
» Artificial intelligence » Reinforcement learning
