Hi maintainers,
I came across keivalya/mini-vla and noticed that it focuses on “a minimal, beginner-friendly VLA to show how robot policies can fuse images, text, and states to generate actions”. That is close to a problem we are working on with EVA-CLIENT, a unified framework for real-robot deployment, evaluation, and LeRobot data collection.
If this direction looks relevant, you can take a look at EVA-CLIENT here: https://github.com/Noietch/EVA-CLIENT
For keivalya/mini-vla, the policy could be exposed through an EVA backend and evaluated across prompts or checkpoints on compatible hardware. A small first experiment could be to wrap one checkpoint as a policy backend and run a small prompt/task sweep. We would be happy to prepare a minimal adapter or example around your current interfaces, rather than asking you to change the project architecture.
If this is useful, I would be glad to compare notes or help with a small integration. If it is not relevant, no worries—and we will not follow up.
Best,
EVA-CLIENT Team
Hi maintainers,
I came across keivalya/mini-vla and noticed that it focuses on “a minimal, beginner-friendly VLA to show how robot policies can fuse images, text, and states to generate actions”. That is close to a problem we are working on with EVA-CLIENT, a unified framework for real-robot deployment, evaluation, and LeRobot data collection.
If this direction looks relevant, you can take a look at EVA-CLIENT here: https://github.com/Noietch/EVA-CLIENT
For keivalya/mini-vla, the policy could be exposed through an EVA backend and evaluated across prompts or checkpoints on compatible hardware. A small first experiment could be to wrap one checkpoint as a policy backend and run a small prompt/task sweep. We would be happy to prepare a minimal adapter or example around your current interfaces, rather than asking you to change the project architecture.
If this is useful, I would be glad to compare notes or help with a small integration. If it is not relevant, no worries—and we will not follow up.
Best,
EVA-CLIENT Team