112 citations · 314 across the 17 of their papers we have counts for
7 papers · 1 filter
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…
PLAS: Latent Action Space for Offline Reinforcement Learning
Wenxuan Zhou, Sujay Bajracharya, David Held
The goal of offline reinforcement learning is to learn a policy from a fixed dataset, without further interactions with the environment. This setting will be an increasingly more i…
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…
Multi-modal Transfer Learning for Grasping Transparent and Specular Objects
Thomas Weng, Amith Pallankize, Yimin Tang +2
State-of-the-art object grasping methods rely on depth sensing to plan robust grasps, but commercially available depth sensors fail to detect transparent and specular objects. To i…
Learning to Optimally Segment Point Clouds
Peiyun Hu, David Held, Deva Ramanan
We focus on the problem of class-agnostic instance segmentation of LiDAR point clouds. We propose an approach that combines graph-theoretic search with data-driven learning: it sea…
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…