Search Results for author: Mozhgan Navardi

Found 2 papers, 0 papers with code

Squeezed Edge YOLO: Onboard Object Detection on Edge Devices

no code implementations18 Dec 2023 Edward Humes, Mozhgan Navardi, Tinoosh Mohsenin

This model is compressed and optimized to kilobytes of parameters in order to fit onboard such edge devices.

Autonomous Navigation Object +2

ReProHRL: Towards Multi-Goal Navigation in the Real World using Hierarchical Agents

no code implementations17 Aug 2023 Tejaswini Manjunath, Mozhgan Navardi, Prakhar Dixit, Bharat Prakash, Tinoosh Mohsenin

In real-world environments with sparse rewards and multiple goals, learning is still a major challenge and Reinforcement Learning (RL) algorithms fail to learn good policies.

reinforcement-learning Reinforcement Learning (RL)

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