Autonomous Systems,
Microsoft AI + Research
Praveen Palanisamy is a Lead Principal AI Engineer and Manager at Microsoft. He currently leads the AI efforts for Project AirSim in the Autonomous Systems, Business AI Incubation group, building an end-to-end (Perception + Scene-Understanding + Prediction + Planning + Control) autonomy platform for autonomous aerial robots and systems using Simulation, Planet-scale Synthetics, Deep Learning and AI. Prior to that, he was an Autonomous Driving AI Researcher at General Motors R&D in Michigan, where, he developed planning and decision making algorithms and architectures using Deep Reinforcement Learning. He is the lead inventor of 70+ patents in the area of autonomous systems. He has authored two practical books – HOIAWOG and TensorFlow 2.x RL Cookbook for use by ML engineers, researchers, students and enthusiasts. He has worked at a few early-stage startups as a tech lead. He obtained his Graduate degree from the Robotics Institute, Carnegie Mellon University, and worked on Autonomous Navigation, Perception and Artificial Intelligence as a Research and Teaching Assistant.
This book contains easy-to-follow recipes for leveraging TensorFlow 2.x to develop artificial intelligence applications. Starting with an introduction to the fundamentals of deep reinforcement learning and TensorFlow 2.x, the book covers OpenAI Gym, model-based RL, model-free RL, and how to develop basic agents. You'll discover how to implement advanced deep reinforcement learning algorithms such as actor-critic, deep deterministic policy gradients, deep-Q networks, proximal policy optimization, and deep recurrent Q-networks for training your RL agents. As you advance, you’ll explore the applications of reinforcement learning by building cryptocurrency trading agents, stock/share trading agents, and intelligent agents for automating task completion. Finally, you'll find out how to deploy deep reinforcement learning agents to the cloud and build cross-platform apps using TensorFlow 2.x.
A paper on prompt-based, procedural generation of diverse datasets for training AI models for aerial autonomy applications. Published in Robotics and Autonomous Systems journal, Volume 166. Link to summary video:https://www.youtube.com/watch?v=eKpOh-K-NfQ. Link to paper.
Spoke at the Commercial Unmanned Aerial Vehicle (CUAV) news webinar on Transforming Infrastructure Inspection with Simulation and Autonomy. I was joined by John McKenna, Co-Founder & CEO of sees.ai and Timothy Reuter from Microsoft. Discussed a few key aspects on how running high-fidelity simulations at scale can accelerate mission planning, software & AI/ML model development and iteration cycles. Leveraging AI and the Autonomy Building blocks including pre-trained models that can be fine-tuned to build custom autonomy modules is a key enabler for accelerating the journey towards aerial autonomy. I also covered some of the key features and focus areas of the Microsoft Project AirSim platform that enables the entire end-to-end pipeline for aerial autonomy. I went over two specific application scenarios: 1. Cell Tower inspection and 2. Bridge inspection. Link to the Webinar page. Link to the recording of the webinar. A snapshot summary is available in the webinar handout slides. Post on LinkedIn.
I’m happy to announce that my book, HOIAWOG! “Hands-On Intelligent Agents with OpenAI Gym: Your guide to developing AI agents using deep reinforcement learning”, made it to the Best Reinforcement Learning eBooks of All Time! compiled by BookAuthority. BookAuthority collects and ranks the best books in the world, and it is a great honor to get this kind of recognition. Thank you for all the reader’s support! You can learn more about the HOIAWOG book here. The source code for all the agents, algorithms and implementation details are available on GitHub. You can get a copy of the book from Amazon.
Building blocks for Autonomous Systems
Jun. 2019 - PresentDeep RL for Autonomous Driving
Jan 2016Autonomous Navigation, Perception & Deep Learning
Aug 2014