User Tools

Site Tools


Upload failed. Maybe wrong permissions?
adrl:people:ffarshidian
Farbod Farshidian

farbodf (at) ethz (dot) ch

Farbod Farshidian is a PhD student at the Agile and Dexterous Robotics Lab (ETH Zürich) since November 2012. His research focuses mainly on the application of reinforcement learning and stochastic optimal control theory in the motion control of legged robots. He received his Master degree in Electrical Engineering from University of Tehran in 2012. His research interests include legged locomotion, motion control, optimal control theory, reinforcement learning, and machine learning.

Publications

[1] Cedric de Crousaz and Farbod Farshidian and Michael Neunert Jonas Buchli (2015). Unified Motion Control for Dynamic Quadrotor Maneuvers Demonstrated on Slung Load and Rotor Failure Tasks. In IEEE International Conference on Robotics and Automation pdf

[2] Farbod Farshidian and Michael Neunert and Jonas Buchli (2014). Learning of Closed-Loop Motion Control. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pdf

[3] Cedric de Crousaz and Farbod Farshidian and Jonas Buchli (2014). Aggressive Optimal Control for Agile Flight with a Slung Load. In IROS 2014 Workshop on Machine Learning in Planning and Control of Robot Motion. pdf

[4] Michael Neunert and Farbod Farshidian and Jonas Buchli (2014). Adaptive Real-time Nonlinear Model Predictive Motion Control. In IROS 2014 Workshop on Machine Learning in Planning and Control of Robot Motion. pdf

[5] Brahayam Ponton and Farbod Farshidian and Jonas Buchli (2014). Learning Compliant Locomotion on a Quadruped Robot. In IROS 2014 Workshop on Compliant Manipulation: Challenges in Learning and Control. pdf

[6] Farbod Farshidian and Jonas Buchli (2013). Path Integral Stochastic Optimal Control for Reinforcement Learning. In The 1st Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM). pdf

[7] Farshidian, Farbod, Zeinab Talebpour, and Majid Nili Ahmadabadi (2012). Budgeted knowledge transfer for state-wise heterogeneous RL agents. Neural Information Processing. Springer Berlin Heidelberg. link

adrl/people/ffarshidian.txt · Last modified: 2018/08/22 11:49 by jonas