aiqbench analysis
HumanHalo advances autonomous navigation by integrating theoretical safety guarantees with data-driven models to handle complex 3D human dynamics, moving beyond simplified 2D approximations. This approach enables real-time execution for micro-air vehicles while maintaining safety without excessive conservatism, even when predicting imperfect human motion. The methodology offers a scalable framework applicable to various robotic platforms seeking safe integration into crowded environments.
HumanHalo: Safe and Efficient 3D Navigation Among Humans via Minimally Conservative MPC
arXiv:2510.17525v4 Announce Type: replace Abstract: Safe and efficient robotic navigation among humans is essential for integrating robots into everyday environments. Most existing approaches focus on simplified 2D crowd navigation and fail to account for the full complexity of human body dynamics beyond root motion. We present HumanHalo, an MPC framework for 3D MAV navigation among humans that combines theoretical safety guarantees with data-driven models for realistic human motion forecasting. Our approach introduces a novel reachability-based safety formulation that constrains only the initial control input for safety while modeling its effects over the entire planning horizon, enabling safe yet efficient navigation. We validate HumanHalo in both simulated experiments using real human trajectories and in the real world, demonstrating its effectiveness across tasks ranging from goal-directed navigation to visual servoing for human tracking. While we apply our method to MAV in this work, it is generic and can be adapted to other platforms. Our results show that the method preserves safety without excessive conservatism, even under imperfect human prediction and in constrained workspaces, while remaining efficient enough for real-time onboard execution.
Abstract:Safe and efficient robotic navigation among humans is essential for integrating robots into everyday environments. Most existing approaches focus on simplified 2D crowd navigation and fail to account for the full complexity of human body dynamics beyond root motion. We present HumanHalo, an MPC framework for 3D MAV navigation among humans that combines theoretical safety guarantees with data-driven models for realistic human motion forecasting. Our approach introduces a novel reachability-based safety formulation that constrains only the initial control input for safety while modeling its effects over the entire planning horizon, enabling safe yet efficient navigation. We validate HumanHalo in both simulated experiments using real human trajectories and in the real world, demonstrating its effectiveness across tasks ranging from goal-directed navigation to visual servoing for human tracking. While we apply our method to MAV in this work, it is generic and can be adapted to other platforms. Our results show that the method preserves safety without excessive conservatism, even under imperfect human prediction and in constrained workspaces, while remaining efficient enough for real-time onboard execution.
Robotics (cs.RO)
arXiv:2510.17525 [cs.RO]
(or arXiv:2510.17525v4 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2510.17525 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 265 words
