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Related Articles from SNS
Grasp-Then-Plan with Failure Attribution: A Closed Two-Stage Framework for Precise and Generalizable Robotic Manipulation
new Abstract: In robotic manipulation, the tight coupling between grasping and motion planning often obscures the true source of failure, leading to inefficient trial-and-error. To enable efficient long-horizon manipulation, we propose GTP-FA (Grasp-Then-Plan with Failure Attribution), a task-oriented two-stage grasp-then-plan framework that generates grasp candidates and performs downstream motion planning conditioned on the selected grasp. Given a failed manipulation trajectory, we learn a...
Controlling energy delivery with bistable nanostructures
arXiv:2506.14266v3 Announce Type: replace-cross Abstract: Countless biological processes are fueled by energy-rich molecules like ATP and GTP that supply energy with extreme efficiency. However, designing similar energy-delivery schemes from the bottom up, essential for the development of powered nanostructures and other \emph{de novo} machinery, presents a significant challenge: how can an energy-rich structure be stable in solution yet still deliver this energy at precisely the right time?...
Matrix nucleotide homeostasis couples energetic state to mitochondrial translation
Mitochondrial protein synthesis must adapt to fluctuations in organellar energetic state to sustain oxidative phosphorylation and cellular homeostasis, yet the mechanisms coupling mitochondrial bioenergetics to gene expression remain poorly understood. Here, we identify matrix nucleotide phosphorylation potential as a direct metabolic determinant of mitochondrial translation. Using ATP synthase inhibition as an experimental perturbation, we show that inhibition of the F1Fo-ATP synthase...
SDTrack: A Baseline for Event-based Tracking via Spiking Neural Networks
Announce Type: replace Abstract: Event cameras provide superior temporal resolution, dynamic range, energy efficiency, and pixel bandwidth. Spiking Neural Networks (SNNs) naturally complement event data through discrete spike signals, making them ideal for event-based tracking. However, current approaches combining Artificial Neural Networks (ANNs) and SNNs suffer from suboptimal architectures that compromise energy efficiency and limit tracking performance.