65 citations · 272 across the 26 of their papers we have counts for
9 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…
GHOST: Hierarchical Sub-Goal Policies for Generalizing Robot Manipulation
Sriram Krishna, Ben Eisner, Haotian Zhan +5
We present GHOST, a framework for learning visuomotor manipulation policies that generalize beyond the training distribution. GHOST factorizes control into (i) a high-level policy…
Dex4D: Task-Agnostic Point Track Policy for Sim-to-Real Dexterous Manipulation
Yuxuan Kuang, Sungjae Park, Katerina Fragkiadaki +1
Learning generalist policies capable of accomplishing a plethora of everyday tasks remains an open challenge in dexterous manipulation. In particular, collecting large-scale manipu…
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…
Gen2Act: Human Video Generation in Novel Scenarios enables Generalizable Robot Manipulation
Homanga Bharadhwaj, Debidatta Dwibedi, Abhinav Gupta +7
How can robot manipulation policies generalize to novel tasks involving unseen object types and new motions? In this paper, we provide a solution in terms of predicting motion info…
Track2Act: Predicting Point Tracks from Internet Videos enables Generalizable Robot Manipulation
Homanga Bharadhwaj, Roozbeh Mottaghi, Abhinav Gupta +1
We seek to learn a generalizable goal-conditioned policy that enables zero-shot robot manipulation: interacting with unseen objects in novel scenes without test-time adaptation. Wh…