iMaC introduces image-based action tokens in a dual-branch architecture to improve future state prediction and control in embodied world models over vector-based baselines.
Predictive red teaming: Breaking policies without breaking robots
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DAERT generates diverse adversarial instructions via a uniform policy in RL to drop VLA task success rates from 93.33% to 5.85% on benchmarks with models like π0 and OpenVLA.
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iMaC: Translating Actions into Motion and Contact Images for Embodied World Models
iMaC introduces image-based action tokens in a dual-branch architecture to improve future state prediction and control in embodied world models over vector-based baselines.
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Uncovering Linguistic Fragility in Vision-Language-Action Models via Diversity-Aware Red Teaming
DAERT generates diverse adversarial instructions via a uniform policy in RL to drop VLA task success rates from 93.33% to 5.85% on benchmarks with models like π0 and OpenVLA.