4 papers · 1 filter
RoboSSM: Scalable In-context Imitation Learning via State-Space Models
Youngju Yoo, Jiaheng Hu, Yifeng Zhu +4
In-context imitation learning (ICIL) enables robots to learn tasks from prompts consisting of just a handful of demonstrations. By eliminating the need for parameter updates at dep…
LodeStar: Long-horizon Dexterity via Synthetic Data Augmentation from Human Demonstrations
Weikang Wan, Jiawei Fu, Xiaodi Yuan +2
Developing robotic systems capable of robustly executing long-horizon manipulation tasks with human-level dexterity is challenging, as such tasks require both physical dexterity an…
Efficient Sensorimotor Learning for Open-world Robot Manipulation
Yifeng Zhu
This dissertation considers Open-world Robot Manipulation, a manipulation problem where a robot must generalize or quickly adapt to new objects, scenes, or tasks for which it has n…
InterPreT: Interactive Predicate Learning from Language Feedback for Generalizable Task Planning
Muzhi Han, Yifeng Zhu, Song-Chun Zhu +2
Learning abstract state representations and knowledge is crucial for long-horizon robot planning. We present InterPreT, an LLM-powered framework for robots to learn symbolic predic…