5 papers
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…
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…
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…
Learn from What We HAVE: History-Aware VErifier that Reasons about Past Interactions Online
Yishu Li, Xinyi Mao, Ying Yuan +3
We introduce a novel History-Aware VErifier (HAVE) to disambiguate uncertain scenarios online by leveraging past interactions. Robots frequently encounter visually ambiguous object…
Generalizable Humanoid Manipulation with 3D Diffusion Policies
Yanjie Ze, Zixuan Chen, Wenhao Wang +5
Humanoid robots capable of autonomous operation in diverse environments have long been a goal for roboticists. However, autonomous manipulation by humanoid robots has largely been…