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cs.LG2026
RAISE: Diagnosing Acquisition Collapse in Costly LLM Signals
Ying Yuan, Yu Wang, Yize Cheng +1
Large language models (LLMs) are increasingly used as costly, on-demand components in real systems, but calling them indiscriminately can waste substantial compute, latency, and se…
cs.RO2026
From Grasps to Dexterity: Large-Scale Grasp Pretraining for Dexterous Manipulation
Ying Yuan, Xinyu Liu, Sriram Krishna +1
Large-scale dexterous grasp datasets encode rich priors over hand-object interaction, but their use has largely been confined to grasp generation and pick-and-place manipulation. W…
cs.RO2026
GHOST: Hierarchical Sub-Goal Policies for Generalizing Robot Manipulation
Sriram Krishna, Ben Eisner, Haotian Zhan +5
We present GHOST, a framework for learning visuomotor manipulation policies that generalize beyond the training distribution. GHOST factorizes control into (i) a high-level policy…