Co-GLANCE distills vision-language models into an end-to-end onboard model for occlusion segmentation and robot allocation, using conformal prediction plus selective abstention to trigger active perception and achieve 25-36% higher accuracy with 350x lower latency than cloud baselines.
Air- ground collaboration for language-specified missions in unknown environments
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
As autonomous robotic systems become increasingly mature, users will want to specify missions at the level of intent rather than in low-level detail. Language is an expressive and intuitive medium for such mission specification. However, realizing language-guided robotic teams requires overcoming significant technical hurdles. Interpreting and realizing language-specified missions requires advanced semantic reasoning. Successful heterogeneous robots must effectively coordinate actions and share information across varying viewpoints. Additionally, communication between robots is typically intermittent, necessitating robust strategies that leverage communication opportunities to maintain coordination and achieve mission objectives. In this work, we present a first-of-its-kind system where an unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV) are able to collaboratively accomplish missions specified in natural language while reacting to changes in specification on the fly. We leverage a Large Language Model (LLM)-enabled planner to reason over semantic-metric maps that are built online and opportunistically shared between an aerial and a ground robot. We consider task-driven navigation in urban and rural areas. Our system must infer mission-relevant semantics and actively acquire information via semantic mapping. In both ground and air-ground teaming experiments, we demonstrate our system on seven different natural-language specifications at up to kilometer-scale navigation.
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A DRL cooperative router, YAML mission API, PX4/ROS 2 stacks, and online rendezvous-aware replanner cut energy-margin violations from 83% to 20% and support outdoor UAV–UGV field missions.
A survey that categorizes LLM uses in multi-robot systems across task allocation, motion planning, action generation, and human interaction, while noting challenges and future research opportunities.
citing papers explorer
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Co-GLANCE: Uncertainty-Aware Active Perception for Heterogeneous Robot Teaming
Co-GLANCE distills vision-language models into an end-to-end onboard model for occlusion segmentation and robot allocation, using conformal prediction plus selective abstention to trigger active perception and achieve 25-36% higher accuracy with 350x lower latency than cloud baselines.
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Towards Reliable Aerial Ground Vehicle Collaboration: An Integrated Planning and Autonomy Framework for Field Deployment
A DRL cooperative router, YAML mission API, PX4/ROS 2 stacks, and online rendezvous-aware replanner cut energy-margin violations from 83% to 20% and support outdoor UAV–UGV field missions.
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Large Language Models for Multi-Robot Systems: A Survey
A survey that categorizes LLM uses in multi-robot systems across task allocation, motion planning, action generation, and human interaction, while noting challenges and future research opportunities.