656 citations · 1.1k across the 55 of their papers we have counts for
7 papers · 1 filter
Adversarial Domain Randomization
Rawal Khirodkar, Kris M. Kitani
Domain Randomization (DR) is known to require a significant amount of training data for good performance. We argue that this is due to DR's strategy of random data generation using…
Domain Randomization for Scene-Specific Car Detection and Pose Estimation
Rawal Khirodkar, Donghyun Yoo, Kris M. Kitani
We address the issue of domain gap when making use of synthetic data to train a scene-specific object detector and pose estimator. While previous works have shown that the constrai…
GroundNet: Monocular Ground Plane Normal Estimation with Geometric Consistency
Yunze Man, Xinshuo Weng, Xi Li +1
We focus on estimating the 3D orientation of the ground plane from a single image. We formulate the problem as an inter-mingled multi-task prediction problem by jointly optimizing…
Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information
Arjun Sharma, Mohit Sharma, Nicholas Rhinehart +1
The use of imitation learning to learn a single policy for a complex task that has multiple modes or hierarchical structure can be challenging. In fact, previous work has shown tha…
Error Correction Maximization for Deep Image Hashing
Xiang Xu, Xiaofang Wang, Kris M. Kitani
We propose to use the concept of the Hamming bound to derive the optimal criteria for learning hash codes with a deep network. In particular, when the number of binary hash codes (…
Understanding hand-object manipulation by modeling the contextual relationship between actions, grasp types and object attributes
Minjie Cai, Kris Kitani, Yoichi Sato
This paper proposes a novel method for understanding daily hand-object manipulation by developing computer vision-based techniques. Specifically, we focus on recognizing hand grasp…
Learning Neural Parsers with Deterministic Differentiable Imitation Learning
Tanmay Shankar, Nicholas Rhinehart, Katharina Muelling +1
We explore the problem of learning to decompose spatial tasks into segments, as exemplified by the problem of a painting robot covering a large object. Inspired by the ability of c…