3 citations · 3 across the 7 of their papers we have counts for
5 papers · 1 filter
: Reactive Real-time Flow Policies
Sungjae Park, Shubham Tulsiani
Generalist manipulation policies increasingly take the form of action-chunking flow policies built on large pretrained backbones. Such chunks run open-loop, so the policy cannot re…
Dex4D: Task-Agnostic Point Track Policy for Zero-Shot Dexterous Manipulation
Yuxuan Kuang, Sungjae Park, Katerina Fragkiadaki +1
Learning generalist policies capable of accomplishing a wide range of everyday tasks remains an open challenge in dexterous manipulation. In particular, collecting large-scale mani…
DemoDiffusion: One-Shot Human Imitation using pre-trained Diffusion Policy
Sungjae Park, Homanga Bharadhwaj, Shubham Tulsiani
We propose DemoDiffusion, a simple method for enabling robots to perform manipulation tasks by imitating a single human demonstration, without requiring task-specific training or p…
Learning to Transfer Human Hand Skills for Robot Manipulations
Sungjae Park, Seungho Lee, Mingi Choi +4
We present a method for teaching dexterous manipulation tasks to robots from human hand motion demonstrations. Unlike existing approaches that solely rely on kinematics information…
DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
Alexander Khazatsky, Karl Pertsch, Suraj Nair +98
The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic manipulation policies. Ho…