activity
20182022
most citedMulti-skill Mobile Manipulation for Object Rearrangement

10 citations · 34 across the 7 of their papers we have counts for

collaborators

9 papers

cs.RO202210 cited

Multi-skill Mobile Manipulation for Object Rearrangement

Jiayuan Gu, Devendra Singh Chaplot, Hao Su +1

We study a modular approach to tackle long-horizon mobile manipulation tasks for object rearrangement, which decomposes a full task into a sequence of subtasks. To tackle the entir…

cs.CV20211 cited

Deep Feedback Inverse Problem Solver

Wei-Chiu Ma, Shenlong Wang, Jiayuan Gu +3

We present an efficient, effective, and generic approach towards solving inverse problems. The key idea is to leverage the feedback signal provided by the forward process and learn…

cs.CV20207 cited

Compositionally Generalizable 3D Structure Prediction

Songfang Han, Jiayuan Gu, Kaichun Mo +4

Single-image 3D shape reconstruction is an important and long-standing problem in computer vision. A plethora of existing works is constantly pushing the state-of-the-art performan…

cs.CV20205 cited

Refactoring Policy for Compositional Generalizability using Self-Supervised Object Proposals

Tongzhou Mu, Jiayuan Gu, Zhiwei Jia +2

We study how to learn a policy with compositional generalizability. We propose a two-stage framework, which refactorizes a high-reward teacher policy into a generalizable student p…

cs.LG20203 cited

Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous Graph Neural Networks

Hao Tang, Zhiao Huang, Jiayuan Gu +2

Current graph neural networks (GNNs) lack generalizability with respect to scales (graph sizes, graph diameters, edge weights, etc..) when solving many graph analysis problems. Tak…

cs.CV20205 cited

Weakly-supervised 3D Shape Completion in the Wild

Jiayuan Gu, Wei-Chiu Ma, Sivabalan Manivasagam +5

3D shape completion for real data is important but challenging, since partial point clouds acquired by real-world sensors are usually sparse, noisy and unaligned. Different from pr…