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arXiv2026

Athena: An Autonomous Open-Hardware Tracked Rescue Robot Platform

In disaster response and situation assessment, robots have great potential in reducing the risks to the safety and health of first responders. As the situations encountered and the required capabilities of the robots deployed in such missions differ wildly and are often not known in advance, heterogeneous fleets of robots are needed to cover a wide range of mission requirements. While UAVs can quickly survey the mission environment, their ability to carry heavy payloads such as sensors and manipulators is limited. UGVs can carry required payloads to assess and manipulate the mission environment, but need to be able to deal with difficult and unstructured terrain such as rubble and stairs. The ability of tracked platforms with articulated arms (flippers) to reconfigure their geometry makes them particularly effective for navigating challenging terrain. In this paper, we present Athena, an open-hardware rescue ground robot research platform with four individually reconfigurable flippers and a reliable low-cost remote emergency stop (E-Stop) solution. A novel mounting solution using an industrial PU belt and tooth inserts allows the replacement and testing of different track profiles. The manipulator with a maximum reach of 1.54m can be used to operate doors, valves, and other objects of interest. Full CAD & PCB files, as well as all low-level software, are released as open-source contributions.

cs.RO↗

arXiv2025

Post-Pruning Accuracy Recovery via Data-Free Knowledge Distillation

Model pruning is a widely adopted technique to reduce the computational complexity and memory footprint of Deep Neural Networks (DNNs). However, global unstructured pruning often leads to significant degradation in accuracy, typically necessitating fine-tuning on the original training dataset to recover performance. In privacy-sensitive domains such as healthcare or finance, access to the original training data is often restricted post-deployment due to regulations (e.g., GDPR, HIPAA). This paper proposes a Data-Free Knowledge Distillation framework to bridge the gap between model compression and data privacy. We utilize DeepInversion to synthesize privacy-preserving ``dream'' images from the pre-trained teacher model by inverting Batch Normalization (BN) statistics. These synthetic images serve as a transfer set to distill knowledge from the original teacher to the pruned student network. Experimental results on CIFAR-10 across various architectures (ResNet, MobileNet, VGG) demonstrate that our method significantly recovers accuracy lost during pruning without accessing a single real data point.

cs.LG↗