Dream-Tac unifies visual and tactile signals in a world action model using contact-gated fusion and attention bias, reporting 31.7% average action accuracy gains on six manipulation tasks.
arXiv preprint arXiv:2512.09927 (2025) FASTER: Rethinking Real-Time Flow VLAs 21
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FASTER adds a Horizon-Aware Schedule to flow VLAs that compresses immediate-action denoising to one step while keeping long-horizon trajectory quality, lowering real-robot reaction latency.
Efficient-WAM delivers 30x lower latency than prior WAMs at 100 ms per chunk while keeping competitive manipulation performance by treating coarse future video as guidance rather than high-fidelity output.
citing papers explorer
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Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot Manipulation
Dream-Tac unifies visual and tactile signals in a world action model using contact-gated fusion and attention bias, reporting 31.7% average action accuracy gains on six manipulation tasks.
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FASTER: Rethinking Real-Time Flow VLAs
FASTER adds a Horizon-Aware Schedule to flow VLAs that compresses immediate-action denoising to one step while keeping long-horizon trajectory quality, lowering real-robot reaction latency.
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Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination
Efficient-WAM delivers 30x lower latency than prior WAMs at 100 ms per chunk while keeping competitive manipulation performance by treating coarse future video as guidance rather than high-fidelity output.