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VITA: Zero-Shot Value Functions via Test-Time Adaptation of Vision-Language Models
🤖AI Summary
Researchers introduce VITA, a zero-shot value function learning method that enhances Vision-Language Models through test-time adaptation for robotic manipulation tasks. The system updates parameters sequentially over trajectories to improve temporal reasoning and generalizes across diverse environments, outperforming existing autoregressive VLM methods.
Key Takeaways
- →VITA addresses limitations of frozen pre-trained VLM representations through lightweight adaptation modules updated at inference time.
- →The method encodes trajectory history into parameters via sequential updates, improving temporal reasoning capabilities.
- →VITA demonstrates superior generalization from single training environments to diverse out-of-distribution robotic tasks.
- →The system's zero-shot value estimates can enhance offline reinforcement learning through reward shaping.
- →Performance on Meta-World benchmark exceeds policies trained with simulation's native dense rewards.
#vision-language-models#reinforcement-learning#robotics#zero-shot-learning#test-time-adaptation#meta-learning#value-functions#ai-research
Read Original →via arXiv – CS AI
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