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cs.RO2026
SynthICL: Scalable In-context Imitation Learning with Synthetic Data
Cheng Qian, Ruomeng Fan, Yifei Ren +2
In-context imitation learning (ICIL) enables robots to learn new tasks from a small number of demonstrations by conditioning a pre-trained policy on task-specific examples, without…
cs.RO2026
Instant-Fold: In-Context Imitation Learning for Deformable Object Manipulation
Yilong Wang, Cheng Qian, Edward Johns
Deformable object manipulation (DOM) is challenging due to high-dimensional, partially observable states that evolve through long-horizon, topology-changing interactions with multi…
cs.RO2026
TransDex: Pre-training Visuo-Tactile Policy with Point Cloud Reconstruction for Dexterous Manipulation of Transparent Objects
Fengguan Li, Yifan Ma, Chen Qian +2
Dexterous manipulation enables complex tasks but suffers from self-occlusion, severe depth noise, and depth information loss when manipulating transparent objects. To solve this pr…