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CORAL: Scalable Multi-Task Robot Learning via LoRA Experts
CORAL proposes a parameter-isolated framework for multi-task robot learning: it freezes a pre-trained VLA backbone and adds one lightweight LoRA expert for each task. It aims to simultaneously address negative transfer…
Summary
CORAL proposes a parameter-isolated framework for multi-task robot learning: it freezes a pre-trained VLA backbone and adds one lightweight LoRA expert for each task. It aims to simultaneously address negative transfer in multi-task learning, forgetting when continuously adding new tasks, and storage overhead in edge deployment.
Problem
- During joint fine-tuning for multi-task robotics, gradients from different tasks can conflict with one another, leading to negative transfer, especially when fine-grained language instructions are easily confused.
- If a full model checkpoint is stored for every task, storage and deployment costs grow linearly with the number of tasks, making this unsuitable for real robot systems.
- Sequentially learning new tasks can also overwrite old knowledge and cause catastrophic forgetting, so a solution is needed that can both scale and avoid mutual interference.
Approach
- Freeze a general pre-trained VLA foundation model and train only a separate LoRA adapter for each task, placing task-specific knowledge into small “experts.”
- LoRA is injected into the attention layers of both the vision-language encoder and the action head, allowing experts to adjust both perception/language features and control policies.
- At inference time, the CORAL Manager determines the task directly from the language instruction and loads the corresponding expert, without requiring a learned gating network or external LLM routing.
- Online switching is implemented by first restoring the clean backbone and then merging the target LoRA weights; after merging, execution follows the original model, so the authors claim there is no additional inference FLOPs or latency overhead.
- Because parameters for different tasks are completely isolated, new tasks can be added sequentially without overwriting old-task parameters, mitigating interference and forgetting by design.
Results
- LIBERO (40 tasks): CORAL with SimVLA achieves an average success rate of 99.3%, an improvement of +0.7 over the SimVLA baseline 98.6%, and also higher than X-VLA 98.1% listed in the paper; on LIBERO-Long, it reaches 98.8% vs 96.4%, an improvement of +2.4.
- LIBERO (same framework, different backbone): CORAL with π0.5 reaches 98.4%, improving over the π0.5 baseline 96.9% by +1.5; within this, the Long subset is 95.8% vs 92.4%, an improvement of +3.4.
- WidowX / Simpler-Bridge: CORAL with SimVLA averages 97.9%, higher than the SimVLA baseline 95.8% by +2.1; it improves by +4.1 on the Stack and Eggplant tasks, and reaches 100%/100% on Spoon/Carrot.
- Google Robot / Simpler-Fractal: CORAL with SimVLA averages 84.9%, improving over the SimVLA baseline 77.0% by +7.9; it is also higher than X-VLA 75.7%. By category: Pick 85.9 (+3.6), Move 92.8 (+11.8), Open 75.9 (+8.2).
- Efficiency and storage: For a 0.8B backbone, a single rank-16 LoRA expert is about 26MB; the authors say this is about 100× smaller than the full model. The expert library for 40 LIBERO tasks is about 1GB, while a single fully fine-tuned checkpoint is about 3GB. Expert switching time is about 100ms, and the paper claims zero additional inference FLOPs.
- Real robot: The paper claims validation on Galaxea R1 Lite for cross-scene generalization, new task acquisition, and resistance to forgetting, but the provided excerpt does not include the full quantitative table for that section, so more specific real-world numbers cannot be listed from the excerpt.
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