1 citations · 1 across the 2 of their papers we have counts for
4 papers
ViTaMIn: Learning Contact-Rich Tasks Through Robot-Free Visuo-Tactile Manipulation Interface
Fangchen Liu, Chuanyu Li, Yihua Qin +3
Tactile information plays a crucial role for humans and robots to interact effectively with their environment, particularly for tasks requiring the understanding of contact propert…
OTTER: A Vision-Language-Action Model with Text-Aware Visual Feature Extraction
Huang Huang, Fangchen Liu, Letian Fu +5
Vision-Language-Action (VLA) models aim to predict robotic actions based on visual observations and language instructions. Existing approaches require fine-tuning pre-trained visio…
Video2Policy: Scaling up Manipulation Tasks in Simulation through Internet Videos
Weirui Ye, Fangchen Liu, Zheng Ding +3
Simulation offers a promising approach for cheaply scaling training data for generalist policies. To scalably generate data from diverse and realistic tasks, existing algorithms ei…
In-Context Imitation Learning via Next-Token Prediction
Letian Fu, Huang Huang, Gaurav Datta +5
We explore how to enhance next-token prediction models to perform in-context imitation learning on a real robot, where the robot executes new tasks by interpreting contextual infor…