962 citations · 2.1k across the 31 of their papers we have counts for
6 papers · 1 filter
Learning with Training Wheels: Speeding up Training with a Simple Controller for Deep Reinforcement Learning
Linhai Xie, Sen Wang, Stefano Rosa +2
Deep Reinforcement Learning (DRL) has been applied successfully to many robotic applications. However, the large number of trials needed for training is a key issue. Most of existi…
Learning with Stochastic Guidance for Navigation
Linhai Xie, Yishu Miao, Sen Wang +5
Due to the sparse rewards and high degree of environment variation, reinforcement learning approaches such as Deep Deterministic Policy Gradient (DDPG) are plagued by issues of hig…
Part-level Car Parsing and Reconstruction from Single Street View
Qichuan Geng, Hong Zhang, Xinyu Huang +5
Part information has been shown to be resistant to occlusions and viewpoint changes, which is beneficial for various vision-related tasks. However, we found very limited work in ca…
Robust Attentional Aggregation of Deep Feature Sets for Multi-view 3D Reconstruction
Bo Yang, Sen Wang, Andrew Markham +1
We study the problem of recovering an underlying 3D shape from a set of images. Existing learning based approaches usually resort to recurrent neural nets, e.g., GRU, or intuitive…
3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations
Zhihua Wang, Stefano Rosa, Bo Yang +3
The ability to interact and understand the environment is a fundamental prerequisite for a wide range of applications from robotics to augmented reality. In particular, predicting…
Defo-Net: Learning Body Deformation using Generative Adversarial Networks
Zhihua Wang, Stefano Rosa, Linhai Xie +4
Modelling the physical properties of everyday objects is a fundamental prerequisite for autonomous robots. We present a novel generative adversarial network (Defo-Net), able to pre…