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

arXiv:2609.34893 · cs.CV, cs.RO · Submitted 2026-09-28 · Read on arXiv

cs.CV, cs.RO

Submitted: 2026-09-28

Updated: 2026-09-28

Project page: https://echo-wam.github.io

Terminology

• World Model — 14× in this paper · explained in 7 episodes
• Grounding — 4× in this paper · explained in 3 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• International Conference on Learning Representations — 2× in this paper
• Latent Representation — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Action-Conditioned Dynamics — 1× in this paper · explained in 1 episode
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Eventaugmented Context with Hindsight and Outlook — 1× in this paper
• Failure Modes — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• International Conference on Machine Learning — 1× in this paper
• Latent Representations — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Sparse Attention — 1× in this paper · explained in 5 episodes

Sources

• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
• OpenVLA: An Open-Source Vision-Language-Action Model
• CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation
• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
• E-VLA: Event-Augmented Vision-Language-Action Model for Dark and Blurred Scenes
• Event-VLA: Action-Conditioned Event Fusion for Robust Vision-Language-Action Model
• WorldVLA: Towards Autoregressive Action World Model
• Ctrl-World: A Controllable Generative World Model for Robot Manipulation
• Latent Action Pretraining from Videos
• CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations
• Motus: A Unified Latent Action World Model
• CoMo: Learning Continuous Latent Motion from Internet Videos for Scalable Robot Learning
• FLARE: Robot Learning with Implicit World Modeling
• $\mu$VLA: On Recurrent Memory for Partially Observable Manipulation in VLA Models
• AtlasVLA: Persistent World-Ego State Modeling for Vision-Language-Action Models
• Mem-World: Memory-Augmented Action-Conditioned World Models for Persistent Robot Manipulation
• SEBVS: Synthetic Event-based Visual Servoing for Robot Navigation and Manipulation
• Event-based Vision for Early Prediction of Manipulation Actions
• REASSEMBLE: A Multimodal Dataset for Contact-rich Robotic Assembly and Disassembly

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