no code implementations • 21 Nov 2022 • Brian Okorn, Chuer Pan, Martial Hebert, David Held
While self-supervised learning has been used successfully for translational object keypoints, in this work, we show that naively applying relative supervision to the rotational group $SO(3)$ will often fail to converge due to the non-convexity of the rotational space.
no code implementations • CVPR 2022 • Ankit Goyal, Arsalan Mousavian, Chris Paxton, Yu-Wei Chao, Brian Okorn, Jia Deng, Dieter Fox
Accurate object rearrangement from vision is a crucial problem for a wide variety of real-world robotics applications in unstructured environments.
no code implementations • 18 Jan 2022 • Qiao Gu, Brian Okorn, David Held
In this paper, we propose the OSSID framework, leveraging a slow zero-shot pose estimator to self-supervise the training of a fast detection algorithm.
1 code implementation • 21 Nov 2021 • Himangi Mittal, Brian Okorn, Arpit Jangid, David Held
The aim of this work is to learn to complete these partial point clouds, giving us a full understanding of the object's geometry using only partial observations.
1 code implementation • 28 Apr 2021 • Brian Okorn, Qiao Gu, Martial Hebert, David Held
We also demonstrate how our system can be used by quickly scanning and building a model of a novel object, which can immediately be used by our method for pose estimation.
1 code implementation • 13 Nov 2020 • YuFei Wang, Gautham Narayan Narasimhan, Xingyu Lin, Brian Okorn, David Held
Current image-based reinforcement learning (RL) algorithms typically operate on the whole image without performing object-level reasoning.
no code implementations • 9 Oct 2020 • Jianing Qian, Junyu Nan, Siddharth Ancha, Brian Okorn, David Held
Current state-of-the-art trackers often fail due to distractorsand large object appearance changes.
1 code implementation • 13 Aug 2020 • Jianing Qian, Thomas Weng, Luxin Zhang, Brian Okorn, David Held
Our approach trains a network to segment the edges and corners of a cloth from a depth image, distinguishing such regions from wrinkles or folds.
Robotics
1 code implementation • 2 Jul 2020 • Brian Okorn, Mengyun Xu, Martial Hebert, David Held
Our first method, which regresses from deep learned features to an isotropic Bingham distribution, gives the best performance for orientation distribution estimation for non-symmetric objects.
1 code implementation • CVPR 2020 • 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.