ReconVLA Framework Enhances Robotic Control with Uncertainty-Guided Vision-Language-Action Models
ReconVLA introduces a conformal prediction method to improve the reliability of vision-language-action (VLA) models in robotic control. These models, which translate visual inputs and language instructions into action sequences, traditionally lack calibrated confidence measures, limiting their real-world application. The framework applies conformal prediction directly to action token outputs from pretrained VLA policies, generating uncertainty estimates that correlate with task success and execution quality. Additionally, it extends conformal prediction to the robot state space to detect unsafe states or outliers before failures occur, providing a failure detection mechanism. This approach addresses the challenge of anticipating uncertainty and failures in dynamic environments, enhancing the safety and dependability of robotic systems. The work is documented in arXiv:2604.16677v1, a cross-announcement abstract, focusing on technical advancements without specifying authors or institutions.
Key facts
- ReconVLA is a conformal model for reliable robotic control
- It addresses uncertainty and failure anticipation in vision-language-action (VLA) models
- The framework applies conformal prediction to action token outputs
- It yields calibrated uncertainty estimates correlating with execution quality
- Conformal prediction is extended to robot state space for failure detection
- The approach detects outliers or unsafe states before failures occur
- VLA models map visual observations and natural language instructions to actions
- The work is detailed in arXiv:2604.16677v1 as a cross-announcement abstract
Entities
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