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Ajna was highlighted on the cover of Science Robotics journal in Aug 2023. The paper can be foundhere for free. This work is a collaboration between the University of Maryland, College Park (PRG-UMD) and Worcester Polytechnic Institute (PeAR-WPI). Please refer to thewiki for instructions on running the code and some research tips and tricks.

Please check out the video for a description of the approach.

We present an approach to estimate uncertainty of any neural network with a loss function that can be adapted to modify any existing loss function for any class of robotics problems. We showcase the approach's efficacy in flying through gaps, navigating through a static environment, dodging dynamic obstacles and segmenting objects from a scene.

• Project Page
• Science Robotics Cover Paper

If you find our work useful please do cite us as follows:

@article{doi:10.1126/scirobotics.add5139,author = {Nitin J. Sanket  and Chahat Deep Singh  and Cornelia Fermüller  and Yiannis Aloimonos },title = {Ajna: Generalized deep uncertainty for minimal perception on parsimonious robots},journal = {Science Robotics},volume = {8},number = {81},pages = {eadd5139},year = {2023},doi = {10.1126/scirobotics.add5139},URL = {https://www.science.org/doi/abs/10.1126/scirobotics.add5139},eprint = {https://www.science.org/doi/pdf/10.1126/scirobotics.add5139},}

• Chahat Deep Singh
• Nitin J Sanket

Copyright (c) 2023 Perception and Robotics Group (PRG-UMD) and Perception and Autonomous Robotics Group (PeAR-WPI)