ReCAPA: Hierarchical Predictive Correction to Mitigate Cascading Failures in VLA Systems
Researchers have introduced ReCAPA, a novel framework designed to address cascading failures in Vision-Language-Action (VLA) systems. Current VLA approaches often suffer from error propagation, where mis-specified intermediate steps accumulate into significant failures during multi-step task execution. ReCAPA, standing for Predictive Alignment and Planning Architecture, mitigates this by employing prediction and contrast mechanisms to adjust deviations across actions, subgoals, and trajectories. The system enforces semantic alignment at all levels using specialized Sinkhorn-based and Score-field modules. This predictive correction jointly updates the action generator during training, ensuring fine-grained steps remain aligned with overall intent. Additionally, the study introduces two new metrics to quantify error propagation and recovery processes in long-horizon tasks. Experimental results demonstrate that ReCAPA achieves competitive performance on embodied agent benchmarks, including VisualAgentBench, MineDojo, and AI2-THOR, outperforming both proprietary and open-source Large Language Model baselines. This advancement highlights significant progress in enhancing the reliability and robustness of autonomous agents in complex multimodal environments.
Wire timeline
ReCAPA: Hierarchical Predictive Correction to Mitigate Cascading Failures in VLA Systems
Researchers have introduced ReCAPA, a novel framework designed to address cascading failures in Vision-Language-Action (VLA) systems. Current VLA approaches often suffer from error propagation, where mis-specified intermediate steps accumulate into significant failures during multi-step task execution. ReCAPA, standing for Predictive Alignment and Planning Architecture, mitigates this by employing prediction and contrast mechanisms to adjust deviations across actions, subgoals, and trajectories. The system enforces semantic alignment at all levels using specialized Sinkhorn-based and Score-field modules. This predictive correction jointly updates the action generator during training, ensuring fine-grained steps remain aligned with overall intent. Additionally, the study introduces two new metrics to quantify error propagation and recovery processes in long-horizon tasks. Experimental results demonstrate that ReCAPA achieves competitive performance on embodied agent benchmarks, including VisualAgentBench, MineDojo, and AI2-THOR, outperforming both proprietary and open-source Large Language Model baselines. This advancement highlights significant progress in enhancing the reliability and robustness of autonomous agents in complex multimodal environments.
cs.AI updates on arXiv.org