14 papers · 1 filter
Failure-Resilient Multi-Robot Coordination with -Connectivity Constrained Submodular Maximization
Yoonjae Hwang, Hojune Kim, Ellen Novoseller +2
Multi-robot coordination is essential in applications such as coverage, target tracking, and information gathering, where robots must jointly select actions to maximize team perfor…
Data-Efficient Adaptation of a Driving VLA to Class 8 Trucks
Satyajeet Das, Aaron Buxbaum, Niels Joubert +1
Class 8 trucks differ from passenger cars in geometry, dynamics, and maneuvering requirements. As a result, vision-language-action (VLA) models trained for passenger vehicles do no…
Learning to Drive on Mars: Visual Multimodal Traversability Estimation for Off-World Navigation
Darren Chiu, Cole Wilson, Andrei Tumbar +2
Autonomous navigation on Mars requires vehicles to distinguish between traversable terrains across diverse and visually challenging environments. However, progress in learning-base…
MA-LIPP: Cooperative Multi-Agent Load-Aware Informative Path Planning for Heterogeneous Robot Teams
Hojune Kim, Guangyao Shi, Gaurav S. Sukhatme
Field robotics missions often require physical samples to be returned to laboratories for analysis, making path planning inherently load-aware and order-dependent as accumulated sa…
EFLUX: Elastic Multi-Robot Formation Navigation and Adaptation with Agentic LLMs
Jinyuan Zhang, Yuwei Wu, Guangyao Shi +3
Multi-robot teams operating in confined or cluttered environments must adapt both their formation geometry and group topology to navigate through complex obstacles. This adaptation…
LIPP: Load-Aware Informative Path Planning with Physical Sampling
Hojune Kim, Guangyao Shi, Gaurav S. Sukhatme
In classical Informative Path Planning (C-IPP), robots are typically modeled as mobile sensors that acquire digital measurements such as images or radiation levels. In this model,…
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…
PIP-LLM: Integrating PDDL-Integer Programming with LLMs for Coordinating Multi-Robot Teams Using Natural Language
Guangyao Shi, Yuwei Wu, Vijay Kumar +1
Enabling robot teams to execute natural language commands requires translating high-level instructions into feasible, efficient multi-robot plans. While Large Language Models (LLMs…
Refinery: Active Fine-tuning and Deployment-time Optimization for Contact-Rich Policies
Bingjie Tang, Iretiayo Akinola, Jie Xu +6
Simulation-based learning has enabled policies for precise, contact-rich tasks (e.g., robotic assembly) to reach high success rates (~80%) under high levels of observation noise an…
SPAR: Scalable LLM-based PDDL Domain Generation for Aerial Robotics
Songhao Huang, Yuwei Wu, Guangyao Shi +2
We investigate the problem of automatic domain generation for the Planning Domain Definition Language (PDDL) using Large Language Models (LLMs), with a particular focus on unmanned…
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…
Towards Embodiment Scaling Laws in Robot Locomotion
Bo Ai, Liu Dai, Nico Bohlinger +7
Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodim…
Hierarchical LLMs In-the-Loop Optimization for Real-Time Multi-Robot Target Tracking under Unknown Hazards
Yuwei Wu, Yuezhan Tao, Peihan Li +4
Real-time multi-robot coordination in hazardous and adversarial environments requires fast, reliable adaptation to dynamic threats. While Large Language Models (LLMs) offer strong…