I'm a researcher in robotics and machine learning. I have a position as a Senior Deep Learning Scientist in the Autonomous Vehicles division at NVidia.
I completed my Ph.D. in the Robotics Embedded Systems Laboratory (RESL) at the University of Southern California under supervision of Gaurav Sukhatme.
My research interests are focused on systems that have to operate and adapt under sparse, noisy and incomplete information.
Most relevant to robotics, this scenario requires careful leveraging of indirect sources of information to make intelligent decisions, such as priors acquired through simulation, additional sensors indirectly (nontrivially) measuring relevant quantities, communication with other agents, different indicators of task progression.
Lately I have been working in the areas of reinforcement and imitation learning, where I am investigating learning under sparse rewards and a sample efficient adaptation to new tasks by combining simulation, automatic task curriculum and meta learning.
I applied my research to a variety of platforms including quadrotors, robotic arms, underwater vehicles and autonomous cars.
A more broad overview of my latest research direction can be found here.
During my Ph.D program at RESL I did a number of internships at:
Facebook AI Research (FAIR) (Summer, 2019) where I was part of the Robotics team and collaborated with Franziska Meier and Edward T. Grefenstette working on the project of meta-learning for RL;
Nvidia (Summer, 2017) where I was part of the Learning & Perception Research team lead by Jan Kautz and collaborated with Stan Birchfield and Jonathan Tremblay working on the project of curriculum learning for RL and the project on learning human-readable plans from demonstrations;
Volkswagen Electronics Research Lab (Summer, 2016) with the research team lead by Lutz Junge and collaborated with Premkumar Natarajan while working on neural network compression;
Blue River Technology (Summer, 2015) where I worked under supervision of Marci Meingast on improving a vision system of the lettuce thinning bot using deep learning.
Besides that, together with Vadim Butakov and Rob Simpson, I worked on a startup called Swerve.ai targeting improvement of safety in autonomous driving by enabling handling at the limits of friction.
I studied Mechanical and Electrical Engineering at Bauman Moscow State Technical University (BMSTU) and Computer Science at the University of Southern California (USC).
In May 2010, I graduated cum laude obtaining Master of Engineering degree at BMSTU.
In May 2015, I received Master of Computer Science degree from USC where I later defended my Ph.D in May 2020.
[10/2021] Our work on RL for Quadrotor Swarms is being presented at CORL 2021: Submission, Poster.
[01/2021] I started my work as a Senior Deep Learning Scientist at NVidia in Nikolai Smolyanskiy's team.
[05/2020] I successfully defended my Ph.D at USC. Infinite gratitude to my committee members: Gaurav Sukhatme, Nora Ayanian, Heather Culbertson and S.K. Gupta.
[03/2020] I ran my second full marathon in Los Angeles within 3h:46m.
[02/2020] USC Viterbi issued an article about our work on drones.
[07/2019] Our work on Sim-to-(Multi)-Real transfer for quadrotors was accepted to IROS 2019 conference.
[06/2019] Started my internship at Facebook AI Research.
[05/2019] Received George Bekey Service Award for contributions in development of our laboratory (RESL).
[04/2019] Our work on Sim-to-(Multi)-Real transfer for quadrotors was selected for a spotlight presentation in the SoCal Robotics Symposium.
[04/2019] Today my student and collaborator Tao Chen was selected for receiving 2019 Viterbi Master’s Award for achievements in research. Tao is a co-author with me on the project of Sim-to-(Multi)-Real transfer for quadrotors. It is a very rewarding feeling for me as a mentor. Good job, Tao!
[07/2018] I ran my first full marathon in San Francisco within 4h:06m.
[05/2018] I successfully passed my Ph.D qualifying exam at USC. Thank you to all my committee members: Gaurav Sukhatme, Stefan Schaal, Joseph Lim, Heather Culbertson and S.K. Gupta.
Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning
International Conference on Robot Learning (CORL), 2021 S. Batra, Z. Huang, A. Petrenko, T. Kumar, A. Molchanov, G. Sukhatme |
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Meta-Learning via Learned Loss
International Conference on Pattern Recognition (ICPR), 2020 S. Bechtle, A. Molchanov, Y. Chebotar, E. Grefenstette, L. Righetti, G. Sukhatme, F. Meier |
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Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors
International Conference on Intelligent Robots and Systems (IROS), 2019 A. Molchanov, T. Chen, W. Hönig, J. Preiss, N. Ayanian, G. Sukhatme |
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Region Growing Curriculum Generation for Reinforcement Learning
2018 A. Molchanov, K. Hausman, S. Birchfield, G. Sukhatme |
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Synthetically Trained Neural Networks for Learning Human-Readable Plans from Real-World Demonstrations International Conference on Robotics and Automation (ICRA), 2018 J. Tremblay, T. To, A. Molchanov, S. Tyree, J. Kautz, S. Birchfield |
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Contact Localization on Grasped Objects using Tactile Sensing
International Conference on Intelligent Robots and Systems (IROS), 2016 A. Molchanov, O. Kroemer , Z. Su, G. S. Sukhatme. |
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Force Estimation and Slip Detection for Grip Control using BioTac International Conference on Humanoid Robotics (Humanoids), 2015 Z. Su, K. Hausman, Y. Chebotar, A. Molchanov, G. Loeb, G. Sukhatme, S. Schaal |
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Active Drifters: Towards a Practical Multi-Robot System for Ocean Monitoring
International Conference on Robotics and Automation (ICRA), 2015 A. Molchanov, A. Breitenmoser, G. Sukhatme |
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Circling the Seas: Design of Lagrangian Drifters for Ocean Monitoring
IEEE Robotics & Automation Magazine (RAM), 2016 S. Subbaraya, A. Breitenmoser, A. Molchanov, Jorg Muller, Carl Oberg, D. Caron, G. Sukhatme |
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Workshop PublicationsMeta Learning via Learned LossICML Workshop on Multi-Task and Lifelong Reinforcement Learning, 2019 S. Bechtle, A. Molchanov, Y. Chebotar, E. Grefenstette, L. Righetti, G. Sukhatme, F. Meier. bibtex pdf Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors Southern California Robotics Symposium, 2019 A. Molchanov, T. Chen, W. Honig, J. A. Preiss, N. Ayanian and G. S. Sukhatme. website bibtex pdf Model-free Contact Localization for Manipulated Objects using Biomimetic Tactile Sensors Humanoids Workshop on Tactile Sensing for Manipulation, 2016 A. Molchanov, O. Kroemer , Z. Su, G. S. Sukhatme. website bibtex pdf BiGS: BioTac Grasp Stability Dataset ICRA Workshop on Grasping and Manipulation Datasets, 2016 Y. Chebotar, K. Hausman, Z. Su, A. Molchanov, O. Kroemer, G. Sukhatme, S. Schaal website bibtex pdf Slip Classification using Tangential and Torsional Skin Distortions on BioTac BMVA Workshop on Visual, Tactile and Force Sensing for Robot Manipulation, 2015 Z. Su, K. Hausman, Y. Chebotar, A. Molchanov, G. Loeb, G. Sukhatme, S. Schaal bibtex pdf Slip Detection and Classification for Grip Control using Multiple Sensory Modalities on BioTac IROS Workshop on Multimodal Sensor-Based Robot Control for HRI and Soft Manipulation, 2015 Z. Su, K. Hausman, Y. Chebotar, A. Molchanov, G. Loeb, G. Sukhatme, S. Schaal bibtex pdf Active drifters: Sailing with the Ocean Currents RSS Workshop on Autonomous Control, Adaptation, and Learning for Underwater Vehicles, 2014 A. Molchanov, A. Breitenmoser, G. S. Sukhatme bibtex pdf |
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Low Friction Areas Localization Using Stereo Cameras and Microphones for Autonomous Driving
Swerve.ai
A. Molchanov, V. Butakov, R. Simpson
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Benchmarking Neural Network Compression Approaches in Application to Semantic Image Segmentation for Autonomous Driving
Volkswagen Electronics Research Lab
A. Molchanov
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Neural Network based Plant Detector and Classifier for Lettuce Thining Bot
Blue River Technology
A. Molchanov, M. Meingast
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Control and State Estimation of a Remotely Operated Underwater Vehicle
Research Institute of Special Mechanical Engineering: Underwater Vehicles Department
K. Chernenko, A. Molchanov, S. Egorov, A. Kutsenko
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