39 citations · 90 across the 9 of their papers we have counts for
11 papers
Deep Projective Rotation Estimation through Relative Supervision
Brian Okorn, Chuer Pan, Martial Hebert +1
Orientation estimation is the core to a variety of vision and robotics tasks such as camera and object pose estimation. Deep learning has offered a way to develop image-based orien…
TAX-Pose: Task-Specific Cross-Pose Estimation for Robot Manipulation
Chuer Pan, Brian Okorn, Harry Zhang +2
How do we imbue robots with the ability to efficiently manipulate unseen objects and transfer relevant skills based on demonstrations? End-to-end learning methods often fail to gen…
IFOR: Iterative Flow Minimization for Robotic Object Rearrangement
Ankit Goyal, Arsalan Mousavian, Chris Paxton +4
Accurate object rearrangement from vision is a crucial problem for a wide variety of real-world robotics applications in unstructured environments. We propose IFOR, Iterative Flow…
OSSID: Online Self-Supervised Instance Detection by (and for) Pose Estimation
Qiao Gu, Brian Okorn, David Held
Real-time object pose estimation is necessary for many robot manipulation algorithms. However, state-of-the-art methods for object pose estimation are trained for a specific set of…
Self-Supervised Point Cloud Completion via Inpainting
Himangi Mittal, Brian Okorn, Arpit Jangid +1
When navigating in urban environments, many of the objects that need to be tracked and avoided are heavily occluded. Planning and tracking using these partial scans can be challeng…
ZePHyR: Zero-shot Pose Hypothesis Rating
Brian Okorn, Qiao Gu, Martial Hebert +1
Pose estimation is a basic module in many robot manipulation pipelines. Estimating the pose of objects in the environment can be useful for grasping, motion planning, or manipulati…
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…
Robust Instance Tracking via Uncertainty Flow
Jianing Qian, Junyu Nan, Siddharth Ancha +2
Current state-of-the-art trackers often fail due to distractorsand large object appearance changes. In this work, we explore the use ofdense optical flow to improve tracking robust…
Cloth Region Segmentation for Robust Grasp Selection
Jianing Qian, Thomas Weng, Luxin Zhang +2
Cloth detection and manipulation is a common task in domestic and industrial settings, yet such tasks remain a challenge for robots due to cloth deformability. Furthermore, in many…
Learning Orientation Distributions for Object Pose Estimation
Brian Okorn, Mengyun Xu, Martial Hebert +1
For robots to operate robustly in the real world, they should be aware of their uncertainty. However, most methods for object pose estimation return a single point estimate of the…
Just Go with the Flow: Self-Supervised Scene Flow Estimation
Himangi Mittal, Brian Okorn, David Held
When interacting with highly dynamic environments, scene flow allows autonomous systems to reason about the non-rigid motion of multiple independent objects. This is of particular…