“最强具身VLA大模型”，究竟强在哪儿？

Core Insights - The core contribution of the π0.6 model lies in its introduction of a more intuitive learning method called RECAP, which allows robots to learn from their mistakes rather than merely imitating correct actions [3][8][24] - The model demonstrates a high success rate of over 90% in tasks such as making espresso, folding clothes, and assembling packaging boxes, showcasing its practical capabilities [1][20] Group 1: RECAP Methodology - RECAP consists of three main phases: offline reinforcement learning (RL) using diverse demonstration data, fine-tuning with human guidance, and online execution where robots learn from sparse rewards and expert corrections [10][20] - The methodology leverages a value function to evaluate actions and an advantage-conditioned strategy to update policies, allowing for efficient learning from both successful and unsuccessful experiences [13][16][42] Group 2: Model Architecture and Performance - The π0.6 model builds upon previous versions, expanding its backbone from Gemma (2.6 billion parameters) to Gemma3 (4 billion parameters), and increasing Action Expert parameters to 860 million [20] - In challenging tasks, RECAP has doubled the throughput (successful task completions per hour) and reduced failure rates by approximately 50% compared to models that only utilized supervised fine-tuning [20] Group 3: Learning from Mistakes - The RECAP approach emphasizes the importance of learning from errors, enabling robots to recover from mistakes through expert intervention and self-correction, which is crucial for real-world applications [24][28] - By utilizing a value function to assess the quality of actions, the model can identify key steps and sources of errors, enhancing its ability to adapt and improve in complex environments [39][41]