ECHO: Event-Augmented Context with Hindsight and Outlook for Wrist-Only Manipulation

Abstract

Learning-based manipulation policies relying on RGB cameras often suffer from degraded observations under extreme exposure. Event cameras mitigate this degradation by asynchronously detecting pixel-level intensity changes to offer a high dynamic range. However, their observations heavily depend on camera placement, as fixed cameras miss static scene content while wrist-mounted camera motion causes previously visited regions to leave the field of view. To address these spatial-temporal limitations, we present ECHO (Event-augmented Context with Hindsight and Outlook), a wrist-only latent world action model that encodes wrist events into compact motion representations to provide temporal and spatial context for policy reasoning. Specifically, ECHO utilizes a pretrained event encoder to explain visual-feature changes between frames. Its hindsight module preserves the gripper trajectory with past event stream as addressable off-camera context. Concurrently, the outlook module introduces learnable event foresight queries supervised to anticipate the event window for future actions, enabling the policy to predict upcoming scene changes. Evaluated on wrist-only RLBench tasks, ECHO outperforms RGB and RGB+event baselines by 20.6 and 12.0 percentage points under normal lighting, and by 14.6 and 11.3 points under severe exposure drops, respectively, while also surpassing RGB references using a third-person camera. Real-world experiments with a wrist-mounted event camera validate that ECHO outperforms RGB-only and RGB+event baselines across multiple tasks under both nominal and severely dark lighting. Project page is at https://echo-wam.github.io/.