Per-Group Error, Not Total MSE: Fine-Tuning Vision-Language-Action Models for 11-DoF Mobile Manipulation
arXiv:2606.00253v1 Announce Type: cross Abstract: Fine-tuning Vision-Language-Action (VLA) models for mobile manipulators with heterogeneous joint spaces can produce a counterintuitive result: the checkpoint with the lowest aggregate MSE is not the one that performs best on the real robot. We argue this is a predictable consequence of collapsing heterogeneous joint groups (arm, gripper, head, wheeled base) into a single metric, where easy-to-predict joints can mask joints that still fail. We fine-tune SmolVLA (450M, action-expert only) on the 11-DoF Toyota HSR and compare it against $\pi_{0.5}
This research addresses immediate challenges in fine-tuning VLA models, a crucial step for deploying advanced robotics in real-world scenarios, leveraging recent advancements in robot learning and large models.
Improving the fine-tuning of Vision-Language-Action models is critical for the reliable and effective deployment of mobile manipulators, directly accelerating the capabilities of humanoid robots and advanced automation.
The understanding of how to evaluate and optimize VLA model performance on heterogeneous robotic platforms shifts from aggregate metrics to group-specific error analysis, leading to more robust and practical robot behaviors.
- · Robotics R&D
- · Automation industry
- · Hardware manufacturers (mobile manipulators)
- · AI model developers
- · Companies relying on naive aggregate performance metrics for robot deployment
More effective fine-tuning methods for complex robotic systems will lead to better real-world performance.
Accelerated development and adoption of mobile manipulation robots in various industries, including logistics and manufacturing.
Enhanced robot capabilities could contribute to broader economic shifts as automated physical labor becomes more sophisticated and pervasive, impacting labor markets and industrial productivity.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG