Portrait de Farnoosh Faraji

Farnoosh Faraji

Doctorat - McGill
Superviseur⋅e principal⋅e
David Meger
Co-supervisor
Gregory Dudek
Sujets de recherche
Apprentissage par renforcement
Apprentissage profond
Google Scholar
GitHub

Publications

Constrained Robotic Navigation on Preferred Terrains Using LLMs and Speech Instruction: Exploiting the Power of Adverbs
Faraz Lotfi
Farnoosh Faraji
Nikhil Kakodkar
Travis Manderson
David Meger
Gregory Dudek
2024-04-02
ArXiv (prépublication)
doi.org
arxiv.org
Uncertainty-aware hybrid paradigm of nonlinear MPC and model-based RL for offroad navigation: Exploration of transformers in the predictive model
Faraz Lotfi
Khalil Virji
Farnoosh Faraji
Lucas Berry
Andrew Holliday
David Meger
Gregory Dudek
In this paper, we investigate a hybrid scheme that combines nonlinear model predictive control (MPC) and model-based reinforcement learning … (voir plus)(RL) for navigation planning of an autonomous model car across offroad, unstructured terrains without relying on predefined maps. Our innovative approach takes inspiration from BADGR, an LSTM-based network that primarily concentrates on environment modeling, but distinguishes itself by substituting LSTM modules with transformers to greatly elevate the performance our model. Addressing uncertainty within the system, we train an ensemble of predictive models and estimate the mutual information between model weights and outputs, facilitating dynamic horizon planning through the introduction of variable speeds. Further enhancing our methodology, we incorporate a nonlinear MPC controller that accounts for the intricacies of the vehicle's model and states. The model-based RL facet produces steering angles and quantifies inherent uncertainty. At the same time, the nonlinear MPC suggests optimal throttle settings, striking a balance between goal attainment speed and managing model uncertainty influenced by velocity. In the conducted studies, our approach excels over the existing baseline by consistently achieving higher metric values in predicting future events and seamlessly integrating the vehicle's kinematic model for enhanced decision-making. The code and the evaluation data are available at https://github.com/FARAZLOTFI/offroad_autonomous_navigation/).
2024-01-01
ICRA (publié)
doi.org
arxiv.org