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CAT's trajectory-level continuous action representation significantly enhances robotic manipulation success rates by eliminating representational redundancy and ensuring temporal consistency.
Grounding action representations in environmental context can drastically improve robotic manipulation performance, especially for complex tasks.
VLA models are surprisingly susceptible to a single adversarial patch that hijacks their visual self-localization, creating a "phantom embodiment" that completely derails their control policy across different architectures and domains.
Cats are helping AI researchers: a Bayesian-inspired model that treats context as a prior significantly improves intent inference for non-speaking agents and avoids shortcut biases.
