monicamrl Goto Github PK

bird-migration_lstm

This was the project I did under Prof. Joelle Pineau.

clean-up-world-gym

OpenAI gym style Clean up world environment for studying Hierarchical RL methods.

driving-preferences

pedestrian_tracking

This was the project I did under Prof. Kaleem Siddiqi

planning-rgrrt

This was the project I did under Prof. David Meger. Abstract: Even the simplest robot model is subject to differential constraint. In robotics most problems involve differential constraints that arise from kinematics and dynamics of the robot. In order to plan a collision free path for robot which it can successfully follow its important to consider these constraints while planning. This project studies a very popular randomized path planning technique called RRT-Planning and apply it for a simple robot model with differential constraints. In order to deal with differential constraints a new sampling technique is studied in the report which is based on feasibility sets. Report also discusses in details theory of reachability sets and discusses reachability sets for simple car motion. Properties of RRTs and how the differential constraint problem affect those properties are also discussed in the report. Finally report also discusses the scope of improvement in the studied algorithms.

radiology_jr

self-driving-sim-gym

OpenAI gym style self-driving car simulator to test RL algorithms.

transfer_club

Repository for the website for our reading group on Transfer Learning and Hierarchical RL in robotics

transferlearning-robotics

This was the project I did under Prof. Doina Precupp. Abstract: Autonomous robots have achieved high levels of performance and reliability at specific tasks. However it is important for a robot agent to be able to adapt to the new environment and learn varying tasks. In Reinforcement learning agent learns by interacting with the environment and gathering data. Therefore learning different tasks in isolation can be very expensive for a physical agent both in terms of computation and actual physical cost of the Robot. Transfer learning can be used in such cases to learn in simulated environment and using the learned knowledge on actual physical robot to avoid damage as well as to speed up vanilla RL algorithms. This report focuses on understanding transfer learning problem in reinforcement learning domain and applying it to robot navigation task.

yorku_project

This was the project I did under Prof. Micheal Jenkin as Mitac Globalink Research intern. Short Summary: During my summer internship at York University I programmed an aquatic agent to drive autonomously on the pond. Project involved localising this aquatic agent on a water body using particle filter where correction feedback on position of the robot was obtained using off-board camera and on board IMU. Position of the robot was tracked using Homography and coloured object tracking. Image shows the user interface I programmed to control the agent. Click on the link in the heading for the video.