141 citations · 305 across the 10 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…
Robot Navigation with Map-Based Deep Reinforcement Learning
Guangda Chen, Lifan Pan, Yu'an Chen +5
This paper proposes an end-to-end deep reinforcement learning approach for mobile robot navigation with dynamic obstacles avoidance. Using experience collected in a simulation envi…
Gesture-based Human-robot Interaction for Field Programmable Autonomous Underwater Robots
Pei Xu
The uncertainty and variability of underwater environment propose the request to control underwater robots in real time and dynamically, especially in the scenarios where human and…
A Real-time Hand Gesture Recognition and Human-Computer Interaction System
Pei Xu
In this project, we design a real-time human-computer interaction system based on hand gesture. The whole system consists of three components: hand detection, gesture recognition a…