I have recently completed a MSc degree in Robotics, Systems and Control at ETH Zurich. Prior to that I worked at Qualcomm with the SNPE SDK team after finishing my undergrad in Software Engineering at McGill.
My interests lie in applying learning methods to robotics related problems. In particular, my main research goals entail enabling dynamical systems to efficiently and safely learn in real environments where they face high prior uncertainties.
I currently work under the supervision of Prof. Yoshua Bengio on motion planning using model-based learning methods.
MSc in Robotics, Systems and Control, 2020
BEng in Software Engineering, 2016
Currently co-organizing a new real robotics challenge to advance the state-of-the-art in robotic manipulation, a key skill required to deploy robots in the real world. Where we host multiple robotic platforms at the Max Planck Institute for Intelligent Systems. Participants will submit their code as they would for a cluster, and it will then be executed automatically on our platforms. This will allow teams to gather hundreds of hours of real robot data with minimal effort.
CausalWorld is an open-source simulation framework and benchmark for causal structure and transfer learning in a robotic manipulation environment (powered by bullet) where tasks range from rather simple to extremely hard. Tasks consist of constructing 3D shapes from a given set of blocks - inspired by how children learn to build complex structures.
A framework of different derivative-free optimizers which can be used in conjunction with a model predictive controller and a learned dynamics model to control an agent in a mujoco or a gym environment.
Proposed a pipeline that comprises one of the most recent approaches, namely the HighResolution Network combined with a lowweight baseline model for extracting the 3D skeleton of human subjects applied to the Human3.6M dataset; splitting the challenge to image-related and geometric-related tasks.
With the increasing complexity of robots and the environments they interact with, crafting manual locomotion controllers becomes a challenging task to achieve. As evident from the recent research in this field, reinforcement learning (RL) contin- ues to emerge as an attractive solution for learning locomotion policies in high- dimensional continuous domains.
Most current exploration algorithms are based on heuristics and sampling/evaluating many potential viewpoints. The goal of this work is to learn an ideal motion to maximize the known space based on a local map representation.
Considered learning online adaptation in a model-based reinforcement learning context where we train a dynamics model, implemented as a Graph Neural Network, in conjunction with using MPC to control a system where the controller adapts to changes in the environment or tasks.
Successfully implemented a path planner and a velocity profiler for duckiebots as part of duckietown, a robotics outreach and educational platform, while taking into account various sources of uncertainty.
Successfully implemented a monocular visual odometry (VO) pipeline with the most essential features: initialization of 3D landmarks, key point tracking between two frames, pose estimation using established 2D ↔ 3D correspondences, and triangulation of new landmarks.
Successfully developed a Telepresence System that is equipped with high definition cameras remotely connected to an Oculus Rift. The purpose of the Telepresence System is to allow the users to see as if they were physically in place of the system itself. The project focused on three main areas: distortion removal, panoramic frame stitching and high definition video streaming.
Designed, built and tested a model of a digital Enigma Machine, a cipher machine used in WWII, on an FPGA board.
Designed a compiler for GoLite, a subset of the Go programming language. Built the entire compiler pipeline (parser, syntax tree, type checker, code generator and optimizer), producing Java Byte Code as the target language.
Created an Artificial Intelligence agent to compete against more than 200 engineering students to play the HUS game, which is from the family of “Mancala Games”, using Reinforcement Learning Techniques. The agent ranked from the top 10%.
Led my team in developing an autonomous robot, in a team of 5 students, to compete against more than 120 engineering students to play catch the flag game, where we ranked 3/25.