5 citations · 7 across the 3 of their papers we have counts for
3 papers
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…
Rethinking Optimization with Differentiable Simulation from a Global Perspective
Rika Antonova, Jingyun Yang, Krishna Murthy Jatavallabhula +1
Differentiable simulation is a promising toolkit for fast gradient-based policy optimization and system identification. However, existing approaches to differentiable simulation ha…
A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation
Rika Antonova, Jingyun Yang, Priya Sundaresan +3
Deformable object manipulation remains a challenging task in robotics research. Conventional techniques for parameter inference and state estimation typically rely on a precise def…