Summary of Betail: Behavior Transformer Adversarial Imitation Learning From Human Racing Gameplay, by Catherine Weaver et al.
BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay
by Catherine Weaver, Chen Tang, Ce Hao, Kenta Kawamoto, Masayoshi Tomizuka, Wei Zhan
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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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 This research proposes a new approach to imitation learning, which enables robots to learn policies from demonstrations without requiring hand-designed reward functions. The method, called BeTAIL (Behavior Transformer Adversarial Imitation Learning), combines the strengths of two existing techniques: Behavior Transformers and Adversarial Imitation Learning. By adding an adversarial residual policy to a behavior transformer policy, BeTAIL can model complex motion sequences and adapt to new environments or distribution shifts. The authors test their approach on three challenges with expert-level demonstrations of real human gameplay in Gran Turismo Sport, achieving improved racing performance and stability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps robots learn from people without needing special rules. They take a good idea that works well for simple tasks but struggles with complex ones and make it better. The new way combines two techniques: one that learns patterns and another that adapts to changes. It’s tested on racing games and does well, even when the robot has learned from different tracks before trying a new one. |
Keywords
* Artificial intelligence * Transformer
