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arXiv 最近完成扫描:2026-11-15 03:37 · 全站最近入库:2026-11-14 15:09各信源进度 →

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新论文 · v1arXiv:2611.15928

Runtime Safety and Emergency Stops on a Full-Size Humanoid: Deployment Lessons and a Case for Graduated Force Authority

Max Conway, Yutong Zhang, Allen Devaraj, William Xie, Alessandro Roncone, Nikolaus Correll

原文摘要

Full-size humanoid robots are being considered for hazardous industrial work such as disassembling electric-vehicle batteries. We describe the safety mechanisms of an open-source autonomy stack for the Unitree H1-2 humanoid and what deploying them taught us. All joint commands pass through a safety layer that runs separately from the control software. It enforces joint position, velocity, and torque limits, and it stops the robot when a state estimate becomes stale or when the hard-wired emergency stop (e-stop) is pressed or disconnected. We pushed the standing robot until it lost balance. With a model-predictive lower-body controller it never recovered, and the operator had to press the e-stop in every trial. Pressing it removes balance control, and the robot falls. The safety layer's own limits trigger the same stop, and limits chosen for manipulation were exceeded during normal walking. Near an operator or a battery pack, the fall can be more dangerous than the cause of the stop. Joint limits bound how much force a robot can apply, but not when applying it is appropriate. Drawing on research on responsibility and on child and adolescent development, we therefore make a case for granting robots force authority in stages, with a person or organization explicitly responsible for its use.

cs.RO

版本更新 · v2arXiv:2509.20333

BBoE: Leveraging Bundle of Edges for Kinodynamic Bidirectional Motion Planning

Srikrishna Bangalore Raghu, Alessandro Roncone

原文摘要

In this work, we introduce BBoE, a bidirectional, kinodynamic, sampling-based motion planner that consistently and quickly finds solutions in environments with varying obstacle clutter. The algorithm combines exploration and exploitation while relying on precomputed robot motions, resulting in efficient convergence towards the goal. Our key contributions include: i) a strategy to quickly find solutions in obstacle-rich spaces by leveraging a bundle of precomputed robot motions (i.e., edges); and ii) BBoE, a robust bidirectional kinodynamic planner that utilizes this strategy to produce fast and feasible solutions. Across four systems spanning 3D to 6D state spaces, BBoE is the only planner to achieve $\geq96\%$ success on every system while attaining the lowest planning time on three of four.

cs.RO