68 citations · 103 across the 7 of their papers we have counts for
7 papers
DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools
Xingyu Lin, Zhiao Huang, Yunzhu Li +3
We consider the problem of sequential robotic manipulation of deformable objects using tools. Previous works have shown that differentiable physics simulators provide gradients to…
Self-supervised Transparent Liquid Segmentation for Robotic Pouring
Gautham Narayan Narasimhan, Kai Zhang, Ben Eisner +2
Liquid state estimation is important for robotics tasks such as pouring; however, estimating the state of transparent liquids is a challenging problem. We propose a novel segmentat…
ROLL: Visual Self-Supervised Reinforcement Learning with Object Reasoning
Yufei Wang, Gautham Narayan Narasimhan, Xingyu Lin +2
Current image-based reinforcement learning (RL) algorithms typically operate on the whole image without performing object-level reasoning. This leads to inefficient goal sampling a…
SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation
Xingyu Lin, Yufei Wang, Jake Olkin +1
Manipulating deformable objects has long been a challenge in robotics due to its high dimensional state representation and complex dynamics. Recent success in deep reinforcement le…
Reinforcement Learning without Ground-Truth State
Xingyu Lin, Harjatin Singh Baweja, David Held
To perform robot manipulation tasks, a low-dimensional state of the environment typically needs to be estimated. However, designing a state estimator can sometimes be difficult, es…
Adaptive Variance for Changing Sparse-Reward Environments
Xingyu Lin, Pengsheng Guo, Carlos Florensa +1
Robots that are trained to perform a task in a fixed environment often fail when facing unexpected changes to the environment due to a lack of exploration. We propose a principled…