5 papers
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…
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…
Observer-Actor: Active Vision Imitation Learning with Sparse-View Gaussian Splatting
Yilong Wang, Cheng Qian, Ruomeng Fan +1
We propose Observer Actor (ObAct), a novel framework for active vision imitation learning in which the observer moves to optimal visual observations for the actor. We study ObAct o…
TAMT: Temporal-Aware Model Tuning for Cross-Domain Few-Shot Action Recognition
Yilong Wang, Zilin Gao, Qilong Wang +3
Going beyond few-shot action recognition (FSAR), cross-domain FSAR (CDFSAR) has attracted recent research interests by solving the domain gap lying in source-to-target transfer lea…
One-Shot Dual-Arm Imitation Learning
Yilong Wang, Edward Johns
We introduce One-Shot Dual-Arm Imitation Learning (ODIL), which enables dual-arm robots to learn precise and coordinated everyday tasks from just a single demonstration of the task…