RETAIN: Robust Robot Policy Finetuning via Parameter Merging

#Robotics #AI #MachineLearning #VLA #RobotLearning #RETAIN #UCBerkeley

Generalist robot policies are incredible, but they face a major hurdle: **overfitting**. When we finetune these policies on a new task with limited data, they often lose their generalist abilities and fail to handle even simple variations of the new task.

In this video, we dive into *RETAIN* (Robust finE-tuning wiTh pArameter mergINg), a simple yet powerful solution from researchers at UC Berkeley. RETAIN allows a robot to learn a new skill—like wiping a whiteboard or placing plates in a rack—while **retaining its broad pretrained knowledge**.

*What is RETAIN?*
The core idea is surprisingly simple: instead of just using the finetuned model, we *linearly interpolate (merge) the weights* of the pretrained generalist model and the newly finetuned model in weight space. This process combines the task-specific expertise of finetuning with the robust generalization of the base model.

*Key Highlights:*
*Robust Generalization:* RETAIN policies succeed in *out-of-distribution (OOD)* scenarios—such as changes in lighting, backgrounds, or object instances—where standard finetuning fails.
*Preserving Generalist Skills:* Unlike naive approaches, RETAIN ensures the robot doesn't forget its prior abilities.
*Continual Learning:* It enables **sequential skill acquisition**, allowing new tasks to be "merged" into the policy one after another without sacrificing old ones.
*Modality Insights:* Research shows that when merging Vision-Language-Action (VLA) models, the *language model parameters* often matter the most for maintaining robustness.

*Real-World Results:*
Tested on real Franka robot arms (DROID dataset) and in simulation (LIBERO), RETAIN achieved a *40% higher success rate* on average compared to prior finetuning methods in novel scenarios.

Whether you are a researcher, a student, or a tech enthusiast, understanding RETAIN is key to building robots that can truly adapt to the messy, unpredictable real world.

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*Analogy for Understanding:*
Think of a generalist robot as a *pro athlete**. If you train them exclusively to play only one specific position on a new team (SFT), they might become stiff and forget their overall athleticism. **RETAIN* is like allowing them to practice that new position while keeping their core athletic "muscle memory" intact, so they can still react to any play on the field.

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*Tags:*
Robotics, AI, RETAIN, Robot Finetuning, Parameter Merging, Vision-Language-Action, VLA, Machine Learning, UC Berkeley, DROID Robot, LIBERO Simulation, Model Merging, Continual Learning, Robotic Manipulation, Overfitting in AI, Generalist Robot Policies, Neural Network Interpolation, AI Research