5 citations · 9 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
Body Transformer: Leveraging Robot Embodiment for Policy Learning
Carmelo Sferrazza, Dun-Ming Huang, Fangchen Liu +2
In recent years, the transformer architecture has become the de facto standard for machine learning algorithms applied to natural language processing and computer vision. Despite n…
MOKA: Open-World Robotic Manipulation through Mark-Based Visual Prompting
Fangchen Liu, Kuan Fang, Pieter Abbeel +1
Open-world generalization requires robotic systems to have a profound understanding of the physical world and the user command to solve diverse and complex tasks. While the recent…
FMB: a Functional Manipulation Benchmark for Generalizable Robotic Learning
Jianlan Luo, Charles Xu, Fangchen Liu +5
In this paper, we propose a real-world benchmark for studying robotic learning in the context of functional manipulation: a robot needs to accomplish complex long-horizon behaviors…