most citedAcquiring Target Stacking Skills by Goal-Parameterized Deep Reinforcement Learning

3 citations · 3 across the 1 of their papers we have counts for

collaborators

6 papers

cs.RO20173 cited

Acquiring Target Stacking Skills by Goal-Parameterized Deep Reinforcement Learning

Wenbin Li, Jeannette Bohg, Mario Fritz

Understanding physical phenomena is a key component of human intelligence and enables physical interaction with previously unseen environments. In this paper, we study how an artif…

cs.CV2016

To Fall Or Not To Fall: A Visual Approach to Physical Stability Prediction

Wenbin Li, Seyedmajid Azimi, Aleš Leonardis +1

Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing nov…

cs.CV2016

Video Interpolation using Optical Flow and Laplacian Smoothness

Wenbin Li, Darren Cosker

Non-rigid video interpolation is a common computer vision task. In this paper we present an optical flow approach which adopts a Laplacian Cotangent Mesh constraint to enhance the…

cs.HC2016

Towards the Design of Effective Freehand Gestural Interaction for Interactive TV

Gang Ren, Wenbin Li, Eamonn O'Neill

As interactive devices become pervasive, people are beginning to looking for more advanced interaction with televisions in the living room. Interactive television has the potential…

cs.CV2016

Blur Robust Optical Flow using Motion Channel

Wenbin Li, Yang Chen, JeeHang Lee +2

It is hard to estimate optical flow given a realworld video sequence with camera shake and other motion blur. In this paper, we first investigate the blur parameterization for vide…

cs.CV2016

Drift Robust Non-rigid Optical Flow Enhancement for Long Sequences

Wenbin Li, Darren Cosker, Matthew Brown

It is hard to densely track a nonrigid object in long term, which is a fundamental research issue in the computer vision community. This task often relies on estimating pairwise co…