Adapting Robot Models with Human Correction Data
micoolcho · x · 2026-07-16
This discussion highlights that while modern vision-language-action (VLA) and world-action models perform well in manipulation, they still struggle to reliably adapt when transferred to new robots and tasks.
The proposed approach uses DAgger-style online imitation learning:
- Deploy the robot first
- Continuously collect human correction data
- Update the policy dynamically, rather than relying solely on offline fine-tuning
The author adds that if similar trajectories or human corrections are available, it would be valuable to scale up the accumulation of DAgger-style data across more tasks and environments.
Related event: FlowDAgger: Adapting Robot Models via Human Corrections(2 posts)→
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