Publication

Control Strategies for Physically Simulated Characters Performing Two-player Competitive Sports

SIGGRAPH


Abstract

In two-player competitive sports, such as boxing and fencing, athletes often demonstrate efficient and tactical movements during a competition. In this paper, we develop a learning framework that generates control policies for physically simulated athletes who have many degrees-of-freedom. Our framework uses a two step-approach, learning basic skills and learning boutlevel strategies, with deep reinforcement learning, which is inspired by the way that people how to learn competitive sports. We develop a policy model based on an encoder-decoder structure that incorporates an autoregressive latent variable, and a mixture-of-experts decoder. To show the effectiveness of our framework, we implemented two competitive sports, boxing and fencing, and demonstrate control policies learned by our framework that can generate both tactical and natural-looking behaviors. We also evaluate the control policies with comparisons to other learning configurations and with ablation studies.

Related Publications

All Publications

ACM Transactions on Applied Perception Journal (ACM TAP) - September 16, 2021

Evaluating Grasping Visualizations and Control Modes in a VR Game

Alex Adkins, Lorraine Lin, Aline Normoyle, Ryan Canales, Yuting Ye, Sophie Jörg

ACM MM - October 20, 2021

EVRNet: Efficient Video Restoration on Edge Devices

Sachin Mehta, Amit Kumar, Fitsum Reda, Varun Nasery, Vikram Mulukutla, Rakesh Ranjan, Vikas Chandra

ICCV - October 11, 2021

Egocentric Pose Estimation from Human Vision Span

Hao Jiang, Vamsi Krishna Ithapu

TSE - June 29, 2021

Learning From Mistakes: Machine Learning Enhanced Human Expert Effort Estimates

Federica Sarro, Rebecca Moussa, Alessio Petrozziello, Mark Harman

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy