384 citations · 616 across the 9 of their papers we have counts for
10 papers
AdaptNet: Policy Adaptation for Physics-Based Character Control
Pei Xu, Kaixiang Xie, Sheldon Andrews +5
Motivated by humans' ability to adapt skills in the learning of new ones, this paper presents AdaptNet, an approach for modifying the latent space of existing policies to allow new…
Composite Motion Learning with Task Control
Pei Xu, Xiumin Shang, Victor Zordan +1
We present a deep learning method for composite and task-driven motion control for physically simulated characters. In contrast to existing data-driven approaches using reinforceme…
Context-Aware Timewise VAEs for Real-Time Vehicle Trajectory Prediction
Pei Xu, Jean-Bernard Hayet, Ioannis Karamouzas
Real-time, accurate prediction of human steering behaviors has wide applications, from developing intelligent traffic systems to deploying autonomous driving systems in both real a…
SocialVAE: Human Trajectory Prediction using Timewise Latents
Pei Xu, Jean-Bernard Hayet, Ioannis Karamouzas
Predicting pedestrian movement is critical for human behavior analysis and also for safe and efficient human-agent interactions. However, despite significant advancements, it is st…
A GAN-Like Approach for Physics-Based Imitation Learning and Interactive Character Control
Pei Xu, Ioannis Karamouzas
We present a simple and intuitive approach for interactive control of physically simulated characters. Our work builds upon generative adversarial networks (GAN) and reinforcement…
Human-Inspired Multi-Agent Navigation using Knowledge Distillation
Pei Xu, Ioannis Karamouzas
Despite significant advancements in the field of multi-agent navigation, agents still lack the sophistication and intelligence that humans exhibit in multi-agent settings. In this…
PFPN: Continuous Control of Physically Simulated Characters using Particle Filtering Policy Network
Pei Xu, Ioannis Karamouzas
Data-driven methods for physics-based character control using reinforcement learning have been successfully applied to generate high-quality motions. However, existing approaches t…
NH-TTC: A gradient-based framework for generalized anticipatory collision avoidance
Bobby Davis, Ioannis Karamouzas, Stephen J. Guy
We propose NH-TTC, a general method for fast, anticipatory collision avoidance for autonomous robots having arbitrary equations of motions. Our proposed approach exploits implicit…
ALAN: Adaptive Learning for Multi-Agent Navigation
Julio Godoy, Tiannan Chen, Stephen J. Guy +2
In multi-agent navigation, agents need to move towards their goal locations while avoiding collisions with other agents and static obstacles, often without communication with each…
Universal power law governing pedestrian interactions
Ioannis Karamouzas, Brian Skinner, Stephen J. Guy
Human crowds often bear a striking resemblance to interacting particle systems, and this has prompted many researchers to describe pedestrian dynamics in terms of interaction force…