14 citations · 22 across the 7 of their papers we have counts for
9 papers
Walk Like This: Stylized Locomotion for Physics-Based Characters through Example-Driven Diffusion
Joshua Aurand, Agon Serifi, Sammy Christen +4
We present a real-time framework for expressive legged locomotion that enables independent control over velocity and style. Unlike existing data-driven controllers, which typically…
Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning
Nikita Rudin, Junzhe He, Joshua Aurand +1
Legged robots are well-suited for navigating terrains inaccessible to wheeled robots, making them ideal for applications in search and rescue or space exploration. However, current…
Close-Proximity Satellite Operations through Deep Reinforcement Learning and Terrestrial Testing Environments
Henry Lei, Joshua Aurand, Zachary S. Lippay +1
With the increasingly congested and contested space environment, safe and effective satellite operation has become increasingly challenging. As a result, there is growing interest…
Stability Analysis of Deep Reinforcement Learning for Multi-Agent Inspection in a Terrestrial Testbed
Henry Lei, Zachary S. Lippay, Anonto Zaman +3
The design and deployment of autonomous systems for space missions require robust solutions to navigate strict reliability constraints, extended operational duration, and communica…
Assessing Autonomous Inspection Regimes: Active Versus Passive Satellite Inspection
Joshua Aurand, Christopher Pang, Sina Mokhtar +3
This paper addresses the problem of satellite inspection, where one or more satellites (inspectors) are tasked with imaging or inspecting a resident space object (RSO) due to poten…
The Safe Trusted Autonomy for Responsible Space Program
Kerianne L. Hobbs, Sean Phillips, Michelle Simon +11
The Safe Trusted Autonomy for Responsible Space (STARS) program aims to advance autonomy technologies for space by leveraging machine learning technologies while mitigating barrier…
Exposure-Based Multi-Agent Inspection of a Tumbling Target Using Deep Reinforcement Learning
Joshua Aurand, Steven Cutlip, Henry Lei +2
As space becomes more congested, on orbit inspection is an increasingly relevant activity whether to observe a defunct satellite for planning repairs or to de-orbit it. However, th…
Mortality and Healthcare: a Stochastic Control Analysis under Epstein-Zin Preferences
Joshua Aurand, Yu-Jui Huang
This paper studies optimal consumption, investment, and healthcare spending under Epstein-Zin preferences. Given consumption and healthcare spending plans, Epstein-Zin utilities ar…
Epstein-Zin Utility Maximization on a Random Horizon
Joshua Aurand, Yu-Jui Huang
This paper solves the consumption-investment problem under Epstein-Zin preferences on a random horizon. In an incomplete market, we take the random horizon to be a stopping time ad…