4 citations · 9 across the 8 of their papers we have counts for
8 papers
CUPID: Curating Data your Robot Loves with Influence Functions
Christopher Agia, Rohan Sinha, Jingyun Yang +5
In robot imitation learning, policy performance is tightly coupled with the quality and composition of the demonstration data. Yet, developing a precise understanding of how indivi…
HoMeR: Learning In-the-Wild Mobile Manipulation via Hybrid Imitation and Whole-Body Control
Priya Sundaresan, Rhea Malhotra, Phillip Miao +7
We introduce HoMeR, an imitation learning framework for mobile manipulation that combines whole-body control with hybrid action modes that handle both long-range and fine-grained m…
Mobi-: Mobilizing Your Robot Learning Policy
Jingyun Yang, Isabella Huang, Brandon Vu +3
Learned visuomotor policies are capable of performing increasingly complex manipulation tasks. However, most of these policies are trained on data collected from limited robot posi…
Unpacking Failure Modes of Generative Policies: Runtime Monitoring of Consistency and Progress
Christopher Agia, Rohan Sinha, Jingyun Yang +4
Robot behavior policies trained via imitation learning are prone to failure under conditions that deviate from their training data. Thus, algorithms that monitor learned policies a…
EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learning
Jingyun Yang, Zi-ang Cao, Congyue Deng +3
Building effective imitation learning methods that enable robots to learn from limited data and still generalize across diverse real-world environments is a long-standing problem i…
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…
Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning
Jingyun Yang, Max Sobol Mark, Brandon Vu +3
The pre-train and fine-tune paradigm in machine learning has had dramatic success in a wide range of domains because the use of existing data or pre-trained models on the internet…
EquivAct: SIM(3)-Equivariant Visuomotor Policies beyond Rigid Object Manipulation
Jingyun Yang, Congyue Deng, Jimmy Wu +3
If a robot masters folding a kitchen towel, we would expect it to master folding a large beach towel. However, existing policy learning methods that rely on data augmentation still…