Summary of Superpadl: Scaling Language-directed Physics-based Control with Progressive Supervised Distillation, by Jordan Juravsky et al.
SuperPADL: Scaling Language-Directed Physics-Based Control with Progressive Supervised Distillation
by Jordan Juravsky, Yunrong Guo, Sanja Fidler, Xue Bin Peng
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Graphics (cs.GR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces SuperPADL, a scalable framework for physics-based text-to-motion that leverages both reinforcement learning (RL) and supervised learning to train controllers on thousands of diverse motion clips. By leveraging progressive distillation, the framework trains controllers in stages, starting with RL-expert policies and then iteratively distilling them into larger, more robust policies using a combination of RL and supervised learning. The resulting SuperPADL controller is trained on over 5000 skills and runs in real-time on a consumer GPU, allowing for natural transitions between skills and interactive animation creation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a way to make character animations by talking to a computer. It uses special models that mimic how people move, which can be controlled using words or text. This helps both experts and non-experts create animations quickly and easily. The problem is that most methods only work well with a few hundred animations, but this new approach learns from thousands of different movements. It does this by combining two learning techniques: one where the computer teaches itself (reinforcement learning), and another where it’s taught by a teacher (supervised learning). The result is an animation controller called SuperPADL that can learn very quickly and create animations in real-time. |
Keywords
» Artificial intelligence » Distillation » Reinforcement learning » Supervised
