The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy
arXiv paper introduces the Unified Autonomy Stack, an open-source system-level solution for resilient autonomy across aerial and ground robots. It integrates multi-modal perception, multi-behavior planning, and multi-layered safe navigation, fusing LiDAR, radar, vision, and inertial data. The stack has been field-tested on rotorcraft and legged robots in smoke-filled, geometrically complex environments, demonstrating robust performance. It enables GNSS-denied navigation, exploration, object discovery, and inspection planning.
[2605.12735] The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy
[Submitted on 12 May 2026]
Title:The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy
View a PDF of the paper titled The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy, by Mihir Dharmadhikari and 9 other authors
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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 this https URL. A video giving the overview of the paper and the field experiments is available at this https URL.
Comments: 35 pages, 22 figures, 8 tables
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.12735 [cs.RO]
(or arXiv:2605.12735v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.12735
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mihir Vinay Kulkarni [view email] [v1] Tue, 12 May 2026 20:39:35 UTC (39,513 KB)
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