17 citations · 33 across the 10 of their papers we have counts for
7 papers · 1 filter
LEARN: Learning End-to-End Aerial Resource-Constrained Multi-Robot Navigation
Darren Chiu, Zhehui Huang, Ruohai Ge +1
Nano-UAV teams offer great agility yet face severe navigation challenges due to constrained onboard sensing, communication, and computation. Existing approaches rely on high-resolu…
Latent Activation Editing: Inference-Time Refinement of Learned Policies for Safer Multirobot Navigation
Satyajeet Das, Darren Chiu, Zhehui Huang +2
Reinforcement learning has enabled significant progress in complex domains such as coordinating and navigating multiple quadrotors. However, even well-trained policies remain vulne…
Compositional Coordination for Multi-Robot Teams with Large Language Models
Zhehui Huang, Guangyao Shi, Yuwei Wu +2
Multi-robot coordination has traditionally relied on a mission-specific and expert-driven pipeline, where natural language mission descriptions are manually translated by domain ex…
HyperPPO: A scalable method for finding small policies for robotic control
Shashank Hegde, Zhehui Huang, Gaurav S. Sukhatme
Models with fewer parameters are necessary for the neural control of memory-limited, performant robots. Finding these smaller neural network architectures can be time-consuming. We…
Collision Avoidance and Navigation for a Quadrotor Swarm Using End-to-end Deep Reinforcement Learning
Zhehui Huang, Zhaojing Yang, Rahul Krupani +3
End-to-end deep reinforcement learning (DRL) for quadrotor control promises many benefits -- easy deployment, task generalization and real-time execution capability. Prior end-to-e…
QuadSwarm: A Modular Multi-Quadrotor Simulator for Deep Reinforcement Learning with Direct Thrust Control
Zhehui Huang, Sumeet Batra, Tao Chen +7
Reinforcement learning (RL) has shown promise in creating robust policies for robotics tasks. However, contemporary RL algorithms are data-hungry, often requiring billions of envir…