AI Analysis Report
Robust Finetuning of Vision-Language-Action Robot Policies via Parameter Merging
This paper introduces RETAIN, a novel finetuning approach for generalist robot policies that uses parameter merging. By interpolating weights of a pre-trained generalist policy and a finetuned policy, RETAIN significantly improves generalization to unseen variations of target tasks and retains broad generalist capabilities. This is especially effective in low-data regimes where traditional finetuning often overfits. The method is shown to be robust and applicable to continual learning, demonstrating superior performance in real-world and simulated experiments for acquiring new robotic skills.
Executive Impact: Key Performance Indicators
RETAIN's innovative approach directly translates into significant improvements for enterprise-grade robotic deployments, enhancing adaptability and operational efficiency.
0%
Generalization Improvement
0%
Retention of Generalist Abilities
Deep Analysis & Enterprise Applications
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RETAIN: Robust Policy Finetuning via Parameter Merging
RETAIN introduces a simple yet highly effective strategy for robustly finetuning generalist robot policies. Unlike standard finetuning which often leads to overfitting and loss of prior knowledge, RETAIN achieves superior generalization and retains broad competencies by merging the weights of the pre-trained generalist policy with the finetuned policy in weight space.
Real-World and Simulated Performance
Extensive evaluations in both simulated (Libero) and real-world (DROID) environments demonstrate RETAIN's effectiveness. It achieves significantly higher success rates (up to 40% improvement) in out-of-distribution (OOD) scenarios and effectively retains generalist capabilities on non-target tasks, showing its applicability for continual learning and robust skill acquisition.
Understanding Parameter Merging
Analysis reveals that model merging in RETAIN helps find solutions that generalize better by combining the breadth of pre-trained knowledge with task-specific adaptations. Modality-specific merging, particularly within the language model backbone of vision-language-action (VLA) policies, is shown to be highly impactful, suggesting more efficient adaptation strategies.
40%
Higher success rate on novel scenarios compared to prior methods.
RETAIN Process Flow
Pre-trained Generalist Policy
→
Finetune on Target Task (Small Dataset)
→
Parameter Merging (Generalist + Finetuned)
→
Robust Finetuned Policy
| Feature | Standard Finetuning | RETAIN (Parameter Merging) |
|---|---|---|
| Generalization to OOD | Limited |
|
| Retention of Generalist Skills | Often lost |
|
| Overfitting on Small Data | High tendency |
|
| Continual Learning | Challenging |
|
Real-World Robotics Application: DROID Tasks
RETAIN was evaluated on DROID robot tasks like 'wiping whiteboard' and 'putting dishes into a drying rack'. With only 50-100 demonstrations, RETAIN consistently outperformed baselines in out-of-distribution (OOD) scenarios. For instance, on the whiteboard task, it achieved near 80% OOD success, similar to ID performance, demonstrating strong generalization.
80%
OOD Success Rate on Whiteboard (DROID)
Calculate Your Potential ROI
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Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Strategic Implementation Roadmap
A phased approach to integrate RETAIN into your existing robotic infrastructure, ensuring a smooth transition and maximal impact.
Phase 1: Initial Assessment & Data Collection
Identify target tasks, collect initial small datasets (50-150 demonstrations), and prepare pre-trained generalist policy for finetuning.
Phase 2: RETAIN Finetuning & Merging
Apply RETAIN for finetuning, interpolating weights of generalist and finetuned policies. Optimize merging coefficient 'α' using a validation OOD scene.
Phase 3: Robustness Validation & Integration
Evaluate the merged policy on diverse OOD scenarios and generalist tasks. Integrate the robust policy into robotic systems for real-world deployment.
Phase 4: Continual Skill Acquisition (Optional)
Sequentially merge new skills into the updated policy checkpoint using RETAIN, enabling lifelong learning and expanding the robot's repertoire.
Strategic Implications & Next Steps
RETAIN represents a significant advancement in robot policy finetuning, offering a path to more capable, adaptable, and cost-effective robotic solutions for the enterprise.
- RETAIN offers a critical solution for adapting generalist robot policies to new tasks robustly, addressing the challenge of overfitting in low-data regimes.
- The ability to preserve generalist capabilities while acquiring new skills supports lifelong learning and continuous adaptation of robotic systems in dynamic environments.
- Modality-specific merging, particularly for language model parameters, suggests avenues for more efficient and targeted policy adaptation.
- The improved generalization to out-of-distribution variations significantly reduces the need for extensive, varied demonstration datasets for new tasks, lowering operational costs and deployment time.
- The inherent robustness of RETAIN-finetuned policies makes them more reliable for real-world deployment, handling unexpected variations with greater success.
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