VLRLA: Deployment-Time Adaptation of Vision-Language-Action Models through In-Context Reinforcement Learning
Vision-language-action (VLA) models connect visual perception and language understanding with robot control, providing a foundation for instruction-conditioned manipulation.
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Key points
- However, conventional pretrained VLA policies lack a mechanism for gradual adaptation through accumulated interaction experience during deployment.
- We therefore propose VLRLA, a general framework that equips existing VLA models with in-context reinforcement learning capabilities for deployment-time adaptation.
- Our approach augments a VLA model with a reinforcement learning module that learns from historical observations, actions, rewards, and termination signals.
- Finally, through extensive experiments, we systematically investigate the learning properties of VLRLA in simulation and demonstrate its ability to adapt to unseen scenarios during real-world deployment.
Sources (1)
- [1]VLRLA: Deployment-Time Adaptation of Vision-Language-Action Models through In-Context Reinforcement LearningarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 03:35 AM
Vision-language-action (VLA) models connect visual perception and language understanding with robot control, providing a foundation for instruction-conditioned manipulation.
However, conventional pretrained VLA policies lack a mechanism for gradual adaptation through accumulated interaction experience during deployment.
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