Michiel van de Panne | DeepAI

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∙ 07/04/2023

Physics-based Motion Retargeting from Sparse Inputs

Avatars are important to create interactive and immersive experiences in...

0 Daniele Reda, et al. ∙

∙ 06/15/2023

Hierarchical Planning and Control for Box Loco-Manipulation

Humans perform everyday tasks using a combination of locomotion and mani...

0 Zhaoming Xie, et al. ∙

∙ 10/24/2022

Understanding the Evolution of Linear Regions in Deep Reinforcement Learning

Policies produced by deep reinforcement learning are typically character...

0 Setareh Cohen, et al. ∙

∙ 10/03/2022

OPT-Mimic: Imitation of Optimized Trajectories for Dynamic Quadruped Behaviors

Reinforcement Learning (RL) has seen many recent successes for quadruped...

0 Yuni Fuchioka, et al. ∙

∙ 05/08/2022

Learning to Brachiate via Simplified Model Imitation

Brachiation is the primary form of locomotion for gibbons and siamangs, ...

0 Daniele Reda, et al. ∙

∙ 04/11/2022

Evaluating Vision Transformer Methods for Deep Reinforcement Learning from Pixels

Vision Transformers (ViT) have recently demonstrated the significant pot...

0 Tianxin Tao, et al. ∙

∙ 03/07/2022

A Survey on Reinforcement Learning Methods in Character Animation

Reinforcement Learning is an area of Machine Learning focused on how age...

7 Ariel Kwiatkowski, et al. ∙

∙ 03/04/2022

Style-ERD: Responsive and Coherent Online Motion Style Transfer

Motion style transfer is a common method for enriching character animati...

0 Tianxin Tao, et al. ∙

∙ 02/06/2022

Exploration with Multi-Sample Target Values for Distributional Reinforcement Learning

Distributional reinforcement learning (RL) aims to learn a value-network...

0 Michael Teng, et al. ∙

∙ 10/28/2021

From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence

Machine learning has long since become a keystone technology, accelerati...

46 Nicholas Roy, et al. ∙

∙ 05/02/2021

Discovering Diverse Athletic Jumping Strategies

We present a framework that enables the discovery of diverse and natural...

3 Zhiqi Yin, et al. ∙

∙ 04/20/2021

GLiDE: Generalizable Quadrupedal Locomotion in Diverse Environments with a Centroidal Model

Model-free reinforcement learning (RL) for legged locomotion commonly re...

6 Zhaoming Xie, et al. ∙

∙ 03/26/2021

Character Controllers Using Motion VAEs

A fundamental problem in computer animation is that of realizing purpose...

0 Hung Yu Ling, et al. ∙

∙ 11/04/2020

Dynamics Randomization Revisited:A Case Study for Quadrupedal Locomotion

Understanding the gap between simulation andreality is critical for rein...

0 Zhaoming Xie, et al. ∙

∙ 10/09/2020

Learning to Locomote: Understanding How Environment Design Matters for Deep Reinforcement Learning

Learning to locomote is one of the most common tasks in physics-based an...

0 Daniele Reda, et al. ∙

∙ 09/22/2020

Learning Task-Agnostic Action Spaces for Movement Optimization

We propose a novel method for exploring the dynamics of physically based...

0 Amin Babadi, et al. ∙

∙ 05/09/2020

ALLSTEPS: Curriculum-driven Learning of Stepping Stone Skills

Humans are highly adept at walking in environments with foot placement c...

0 Zhaoming Xie, et al. ∙

∙ 12/06/2019

Learning to Correspond Dynamical Systems

Correspondence across dynamical systems can lend us better tools for lea...

0 Nam Hee Kim, et al. ∙

∙ 03/22/2019

Iterative Reinforcement Learning Based Design of Dynamic Locomotion Skills for Cassie

Deep reinforcement learning (DRL) is a promising approach for developing...

0 Zhaoming Xie, et al. ∙

∙ 04/17/2018

Terrain RL Simulator

We provide 89 challenging simulation environments that range in difficul...

0 Glen Berseth, et al. ∙

∙ 04/08/2018

DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills

A longstanding goal in character animation is to combine data-driven spe...

0 Xue Bin Peng, et al. ∙

∙ 03/15/2018

Feedback Control For Cassie With Deep Reinforcement Learning

Bipedal locomotion skills are challenging to develop. Control strategies...

0 Zhaoming Xie, et al. ∙

∙ 02/13/2018

Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control

Deep reinforcement learning has demonstrated increasing capabilities for...

0 Glen Berseth, et al. ∙

∙ 01/11/2018

Model-Based Action Exploration

Deep reinforcement learning has great stride in solving challenging moti...

0 Glen Berseth, et al. ∙

∙ 11/03/2016

Learning Locomotion Skills Using DeepRL: Does the Choice of Action Space Matter?

The use of deep reinforcement learning allows for high-dimensional state...

0 Xue Bin Peng, et al. ∙

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