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General-purpose robots must infer what a new task requires and translate that understanding into appropriate physical action. In-context learning (ICL) for robots supports this process by using demonstrations and interaction to direct existing competence with neural parameters held fixed during deployment. We organize...
Robo-Harness K1 is introduced, a robot-use agent (RUA) framework that exposes perception as tools that makes 3D geometry accessible without changing the VLM architecture or training a depth encoder, and suggests that perception-augmented RUAs offer a promising route to sample-efficient, generalizable robotic policies t...
Ada-BPSG is introduced, a line-search-free BPSG method that couples the SAGA gradient table with a stabilized Barzilai--Borwein (BB) candidate and yields a direct analytical chain from relative smoothness and component-wise variance control to convergence in finite-dimensional normed spaces.
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