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ART is a tool-injection framework that tunes any VLA model to leverage off-the-shelf tool modules for low-level vision, high-level affordance, and embodiment enhancement, and ART reduces the complexity of the action solution space through tool-use, which improves generalizability across different tasks but also reduces...
Results indicate that combining terrain-conditioned neural kinematics, optimization-based planning, and language-guided adaptation yields data-efficient and robust autonomy for articulated tracked robots.
In urban search and rescue, articulated tracked robots (ATRs) must traverse structured but contact-rich environments such as stairwells and cluttered building interiors. Reliable autonomy remains challenging because robot-terrain interaction (RTI) is hybrid and discontinuous, and effective flipper-track coordination is...
Retrieve in Time, Correct in Frequency (RTCF), a training-free test-time correction framework that improves frozen VLA performance with low model-side overhead and requires no parameter updates, repeated VLA inference, or additional GPU resources.
HerA (Historical Evidence Routing Adapter) is introduced, a framework for routing retained historical evidence into a frozen latent predictor, and RRPM, a lightweight adapter comprising a Structured Memory Bank, Memory Registers, and Workspace Registers support historical evidence routing as a practical adaptation stra...
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