17 citations · 33 across the 10 of their papers we have counts for
11 papers
MILO: Automated Harness Discovery via Orchestrated Multi-Agent Evolution
Prithwish Jana, Mononito Goswami, Hao Liu +9
Modern agentic systems combine an AI model with a harness that controls execution and environmental interactions. Harness design strongly affects long-horizon performance, yet its…
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…
Can Large Language Models Solve Robot Routing?
Zhehui Huang, Guangyao Shi, Gaurav S. Sukhatme
Routing problems are common in mobile robotics, encompassing tasks such as inspection, surveillance, and coverage. Depending on the objective and constraints, these problems often…
Guaranteed Trust Region Optimization via Two-Phase KL Penalization
K. R. Zentner, Ujjwal Puri, Zhehui Huang +1
On-policy reinforcement learning (RL) has become a popular framework for solving sequential decision problems due to its computational efficiency and theoretical simplicity. Some o…
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…
Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning
Sumeet Batra, Zhehui Huang, Aleksei Petrenko +3
We demonstrate the possibility of learning drone swarm controllers that are zero-shot transferable to real quadrotors via large-scale multi-agent end-to-end reinforcement learning.…
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning
Aleksei Petrenko, Zhehui Huang, Tushar Kumar +2
Increasing the scale of reinforcement learning experiments has allowed researchers to achieve unprecedented results in both training sophisticated agents for video games, and in si…