Robotics Research Focuses on Universal Post-Training to Enhance Reliability and Deployment
The field of robotics is currently at a critical juncture, similar to where language modeling was with GPT-2, according to recent research. While pretrained models like vision-language-action models (VLAs) and world-action models (WAMs) can execute complex behaviors, their reliability for real-world deployment remains a significant challenge. A robot that performs correctly 95% of the time is still prone to errors in dynamic environments, making it unsuitable for autonomous tasks in homes or factories. The key missing element is a robust 'post-training' recipe, which involves models learning from their own experiences to improve reliability. Unlike language models, where errors can be caught by human reviewers, a bad robot action has immediate physical consequences. Current reinforcement learning (RL) methods for robotics face challenges due to the high cost of generating data and the long-horizon nature of tasks, where rewards are often only observed at the very end of a sequence of actions. Existing valu...
