most citedLearning Whole-body Motor Skills for Humanoids

21 citations · 22 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV20221 cited

NeuralUDF: Learning Unsigned Distance Fields for Multi-view Reconstruction of Surfaces with Arbitrary Topologies

Xiaoxiao Long, Cheng Lin, Lingjie Liu +5

We present a novel method, called NeuralUDF, for reconstructing surfaces with arbitrary topologies from 2D images via volume rendering. Recent advances in neural rendering based re…

cs.GR2022

Real-Time Style Modelling of Human Locomotion via Feature-Wise Transformations and Local Motion Phases

Ian Mason, Sebastian Starke, Taku Komura

Controlling the manner in which a character moves in a real-time animation system is a challenging task with useful applications. Existing style transfer systems require access to…

cs.GR2020

Technical Note: Generating Realistic Fighting Scenes by Game Tree

Hubert P. H. Shum, Taku Komura

Recently, there have been a lot of researches to synthesize / edit the motion of a single avatar in the virtual environment. However, there has not been so much work of simulating…

cs.RO2020

Learning natural locomotion behaviors for humanoid robots using human knowledge

Chuanyu Yang, Kai Yuan, Shuai Heng +2

This paper presents a new learning framework that leverages the knowledge from imitation learning, deep reinforcement learning, and control theories to achieve human-style locomoti…

cs.RO202021 cited

Learning Whole-body Motor Skills for Humanoids

Chuanyu Yang, Kai Yuan, Wolfgang Merkt +3

This paper presents a hierarchical framework for Deep Reinforcement Learning that acquires motor skills for a variety of push recovery and balancing behaviors, i.e., ankle, hip, fo…