Real-Time EXPO-FT: Slow VLA, Fast Corrections, Better Control
Async RL fine-tuning via EXPO-FT enables π0.5 to handle highly dynamic manipulation tasks such as ball balancing and striking in real time. The approach demonstrates that post-training with RL can unlock dynamic capabilities not achievable through imitation learning alone.
·
29:07
|
Async RL fine-tuning via EXPO-FT enables π0.5 to handle highly dynamic manipulation tasks such as ball balancing and striking in real time. The approach demonstrates that post-training with RL can unlock dynamic capabilities not achievable through imitation learning alone.
In this episode of Embodied AI 101, we explore "Real-Time EXPO-FT: Slow VLA, Fast Corrections, Better Control". We break down the research, methodology, and real-world implications for robotics, AI, and physical intelligence.
Embodied AI 101 covers the latest research at the intersection of AI and physical intelligence — robotics, manipulation, world models, and the path from digital intelligence to embodied agents.
