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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.
Future tactile states can be predicted more effectively from intermediate action features, transforming how we approach tactile supervision in robotic manipulation.
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.
