Echo: Jointly Denoising Action and Task Status Signals for Vision-Language-Action Models

Echo proposes a framework that treats two task status signals, stop and failure, as two dimensions of each action chunk and denoises them together with robot actions, thereby distinguishing ongoing execution, successful completion, and failure. The method requires no frame-level progress annotations; it provides complementary supervision by constructing successful, failed, and recovery trajectories, and it lets status signals denoise faster than actions so that subsequent action denoising steps can build on cleaner status estimates. Real-world manipulation experiments show that Echo accurately predicts task status and consistently improves policy performance.