The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy

• URL: http://arxiv.org/abs/2605.12735v1
• Date: Tue, 12 May 2026 20:39:35 GMT
• Title: The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy
• Abstract summary: We introduce and open-source the Unified Autonomy Stack, a system-level solution that enables resilient autonomy across aerial and ground robot morphologies.<n>The architecture centers on three modules -- multi-modal perception, multi-behavior planning, and multi-layered safe navigation.<n>The stack has been field-tested and validated on both aerial (rotorcraft) and ground (legged) robots.
• Score: 13.105188128582567
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce and open-source the Unified Autonomy Stack, a system-level solution that enables resilient autonomy across diverse aerial and ground robot morphologies. The architecture centers on three synergistic modules -- multi-modal perception, multi-behavior planning, and multi-layered safe navigation -- that together deliver comprehensive mission autonomy. The stack fuses data from LiDAR, radar, vision, and inertial sensing, enabling (a) robust localization and mapping through factor graph-based fusion, (b) semantic scene understanding, (c) motion and informative path planning through sampling-based techniques adaptive across spatial scales, as well as (d) multi-layered safe navigation both through planning on the online reconstructed map and deep learning-driven exteroceptive policies alongside last-resort safety filters using control barrier functions. The resulting behaviors include safe GNSS-denied navigation into unknown and perceptually-degraded regions, exploration of complex environments, object discovery, and efficient inspection planning. The stack has been field-tested and validated on both aerial (rotorcraft) and ground (legged) robots operating in a host of demanding environments, including self-similar and smoke-filled settings, with complex geometries and high obstacle clutter. These tests demonstrate resilient performance in challenging conditions. To facilitate ease of adoption, we open-source the implementation alongside supporting documentation, validation, and evaluation datasets https://github.com/ntnu-arl/unified_autonomy_stack. A video giving the overview of the paper and the field experiments is available at https://youtu.be/l8Su8OXsM-E.

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