arXiv:2608. 13438v1 Announce Type: cross Abstract: Contact-rich manipulation failures are often detected only after the robot has committed to contact.
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arXiv:2606. 08508v1 Announce Type: cross Abstract: Generative robot policies fail unpredictably at deployment: they hesitate at critical moments, drift off-task, or commit to unrecoverable actions.
Agile-WAM is a tactile World Action Model that jointly predicts future visual and tactile states and robot actions for contact‑rich manipulation. It encodes visual and tactile observations into a shared latent space and uses a vision‑tactile‑to‑action flow‑matching process to generate action chunks and future latents. The model introduces multi‑horizon multimodal prediction, leveraging the different timescales of vision and touch, and achieves a 29.4 % improvement in real‑world success rates with 11.9 ms inference latency across nine simulated and five real‑world tasks.
arXiv:2608. 11605v1 Announce Type: new Abstract: World Action Models (WAMs) couple future visual prediction with robot action generation, enabling policies to model how the physical world evolves during interaction.
arXiv:2606. 15768v1 Announce Type: cross Abstract: Vision-Language-Action models (VLAs) leverage large-scale vision-language pretraining for semantic robot control, but often lack explicit foresight into how robot actions change the scene.
arXiv:2607. 29169v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies achieve strong performance in robotic manipulation but remain vulnerable to runtime disturbances that break the temporal alignment among visual observations, robot states, and executed actions.
World models offer a promising route toward robot planning by enabling agents to imagine and verify the consequences of actions before execution. However, current video-based world models often struggle to capture the physical constraints that govern manipulation, particularly contact.