Agile-WAM: An Agile Tactile World Action Model for Contact-Rich Robot Control
Researchers introduce Agile-WAM, a tactile world action model using direct vision-tactile-to-action flow matching for agile contact-rich robot control.
Agile-WAM encodes visual and tactile observations into a shared latent and jointly generates action chunks plus future visual and tactile latents via flow matching, avoiding large pretrained generative backbones. Multi-horizon multimodal prediction supervises visual latents at longer offsets while capturing abrupt tactile contact dynamics in the next frame. Across nine simulated and five real-world manipulation tasks it achieved a 29.4% relative success-rate gain over the strongest baseline with 11.9 ms inference latency.
- Flow matching jointly generates action chunks and future latents
- Multi-horizon prediction handles differing visual and tactile timescales
- 29.4% relative success-rate gain in real-world experiments
- 11.9 ms inference latency enables high-frequency robot control
From the original article
1 min read at arxiv.orgWorld Action Models (WAMs) advance beyond conventional visuomotor policies by jointly predicting future world states and robot actions, enabling the policy to learn physical dynamics that support effective control. However, recent tactile WAMs often rely on large-scale pretrained generative backbones to capture contact-rich physical dynamics, which limit their inference efficiency and flexible deployment.
By Hanchu Zhou · arxiv.org
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