Robotics Research Focuses on Universal Post-Training to Enhance Reliability of Advanced Models
Current robotics research is heavily focused on developing a 'universal post-training recipe' to improve the reliability and deployability of advanced robotics models, such as vision-language-action models (VLAs) and world-action models (WAMs). These models, while capable of complex behaviors due to extensive pre-training, often lack the consistent reliability required for real-world applications. Researchers note that a robot performing a task correctly 95% of the time is still prone to errors that could be problematic in environments like homes or factories. The challenge lies in enabling these models to learn effectively from their own experiences, similar to how post-training techniques like supervised instruction tuning and reinforcement learning from human feedback transformed large language models (LLMs) from impressive demos into practical tools. The goal is to move beyond the current 'craft' approach to robotics training and establish standardized, reliable methods for fine-tuning.
