profile photo

Chris Agia

I'm a first-year PhD student in Computer Science at Stanford University and a member of the Stanford Artificial Intelligence Laboratory. I'm on rotation with Jeannette Bohg and Jiajun Wu - affiliated with IPRL and SVL. My research is generously funded by the Stanford School of Engineering Fellowship.

My research seeks to extend the capabilities of AI-driven perception, planning and control systems to position robots for success in long-horizon, complex task settings. Thus, my recent work has intersected 3D scene representations & planning, and robot vision & control.

Each week, I set aside a few hours for mentorship or to discuss research topics in AI & Robotics. Feel free to book an open slot>.

I graduated from the Engineering Science, Robotics program at the University of Toronto, where I was advised by Florian Shkurti at RVL and the Vector Institute. I've been fortunate to collaborate with Liam Paull at MILA, David Meger and Gregory Dudek at McGill, Goldie Nejat at the University of Toronto, and colleagues from Microsoft Research and Facebook AI Research. In industry, I had the opportunity to work on multi-agent reinforcement learning in mixed reality environments at Microsoft, perception and localization for self-driving vehicles at Huawei Noah's Ark Lab with Bingbing Liu, and language-agnostic ABI simulators at Google Cloud.

Email  /  CV  /  LinkedIn  /  GitHub

clean-usnob Ph.D in Computer Science
Department of Computer Science, Stanford University
Sep 2021 | Stanford, CA

School of Engineering Fellowship
clean-usnob B.A.Sc in Engineering Science, Robotics
Faculty of Applied Science and Engineering, University of Toronto
Sep 2016 - May 2021 | Toronto, ON

President's Scholarship Program
NSERC Undergraduate Research Award
Dean's Honour List - 2018-2021


I'm interested bridging concepts from Robotics, Deep Learning, and Computer Vision to build improved task & motion planning, decision-making and control systems. I've recently explored modern learning-based planners and their amenability to long-horizon robotic tasks in large-scale 3D scene graphs - details in BASc thesis. Before that, I researched couplings in representation learning and reinforcement learning to create observational models that facilitate improved control.

I've also lead and contributed to projects related to: semantic localization, 3D scene understanding, 3D semantic scene completion, 2D/3D object detection, LiDAR segmentation, and more!

Journal Papers
clean-usnob Lightweight Semantic-aided Localization with Spinning LiDAR Sensor
Yuan Ren*, Bingbing Liu, Ran Cheng, Christopher Agia
[Patented]. IEEE Transactions on Intelligent Vehicles (T-IV), 2021
PDF / IEEExplore

How can semantic information be leveraged to improve localization accuracy in changing environments? We present a robust LiDAR-based localization algorithm that exploits both semantic and geometric properties of the scene with an adaptive fusion strategy.

A Sim-to-Real Pipeline for Deep Reinforcement Learning for Autonomous Robot Navigation in Cluttered Rough Terrain
Han Hu*, Kaicheng Zhang*, Aaron Hao Tan, Michael Ruan, Christopher Agia, Goldie Nejat
IEEE Robotics and Automation Letters (RA-L) at IROS, 2021 | Prague, CZ
PDF / Video / IEEExplore

Deep Reinforcement Learning is effective for learning robot navigation policies in rough terrain and cluttered simulated environments. In this work, we introduce a series of techniques that are applied in the policy learning phase to enhance transferability to real-world domains.

Conference Papers
clean-usnob Taskography: Evaluating Robot Task Planning over Large 3D Scene Graphs
Christopher Agia*, Krishna Murthy Jatavallabhula*, Mohamed Khodeir, Ondrej Miksik, Mustafa Mukadam, Vibhav Vineet, Liam Paull, Florian Shkurti
Conference on Robot Learning (CoRL), 2021 | London, UK
PDF / Poster / OpenReview / Project Site

3D Scene Graphs (3DSGs) are informative abstractions of our world that unify symbolic, semantic, and metric scene representations. We present a benchmark for robot task planning over large 3DSGs and evaluate classical and learning-based planners; showing that real-time planning requires 3DSGs and planners to be jointly adapted to better exploit 3DSG hierarchies.

clean-usnob Latent Attention Augmentation for Robust Autonomous Driving Policies
Ran Cheng*, Christopher Agia*, David Meger, Florian Shkurti, Gregory Dudek
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021 | Prague, CZ
PDF / IEEExplore

Learning visual state representations can significantly reduce the strain on policy learning from high-dimensional images. In this paper, we propose a framework to inform and guide policy learning with augmented attention representations, demonstrating outstanding convergence speeds and stability for self-driving control.

clean-usnob S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds
Ran Cheng*, Christopher Agia*, Yuan Ren, Bingbing Liu
[Patented]. Conference on Robot Learning (CoRL), 2020 | Cambridge, US
PDF / Talk / Video / arXiv

Small-scale semantic reconstruction methods have had little success in large outdoor scenes as a result of exponential increases in sparsity, and a computationally expensive design. We propose a sparse convolutional network architecture based on the Minkowski Engine, achieving state-of-the-art results for semantic scene completion in 2D/3D space from LiDAR point clouds.

clean-usnob Depth Prediction for Monocular Direct Visual Odometry
Ran Cheng*, Christopher Agia, David Meger, Gregory Dudek
Conference on Computer and Robotic Vision (CRV), 2020 | Ottawa, ON
PDF / Talk / Poster / IEEExplore

Direct methods are able to track motion with considerable long-term accuracy. However, scale inconsistent estimates arise from random or unit depth initialization. We integrate dense depth prediction with the Direct Sparse Odometry system to accelerate convergence in the windowed bundle-adjustment and promote estimates with consistent scale.


Several methods from Conference / Journal Papers contain patented components as well (i.e. indicated by [Patented]).

clean-usnob Road Surface Semantic Segmentation from LiDAR Point Clouds
Christopher Agia*, Ran Cheng, Yuan Ren, Bingbing Liu
[Patented], 2020

Long-range sparsity in point clouds constitutes a challenge for accurate LiDAR-based road estimation. This invention leverages bird's eye view features learned directly from aggregated point clouds and refines them with a convolutional CRF to semantically segment roads and predict surface elevation with high precision.

clean-usnob Software Engineering Intern
Microsoft, Mixed Reality and Robotics
May 2021 - Aug 2021 | Redmond, Washington

Research & development at the intersection of mixed reality, artificial intelligence, and robotics. Created a process unlocking the training and HL2 deployment of multi-agent reinforcement learning scenarios in shared digital spatial-semantic representations with Scene Understanding.

clean-usnob Robotics & ML Researcher
Vector Institute, Robot Vision and Learning Lab | Advised by Prof. Florian Shkurti
Department of Computer Science, University of Toronto
May 2020 - Apr 2021 | Toronto, ON

Research in artificial intelligence and robotics. Topics include task-driven perception via learning map representations for downstream control tasks with graph neural networks, and visual state abstraction for Deep Reinforcement Learning based self-driving control.

clean-usnob Software Engineering Intern
Google, Cloud
May 2020 - Aug 2020 | San Francisco, CA

Designed a Proxy-Wasm ABI Test Harness and Simulator that supports both low-level and high-level mocking of interactions between a Proxy-Wasm extension and a simulated host environment, allowing developers to test plugins in a safe and controlled environment.

clean-usnob Robotics & ML Research Intern
Mobile Robotics Lab | Supervised by Prof. David Meger, Prof. Gregory Dudek
School of Computer Science, McGill University
Jan 2020 - May 2020 | Toronto, ON

Machine learning and robotics research on the topics of Visual SLAM and Deep Reinforcement Learning in collaboration with the Mobile Robotics Lab.

clean-usnob Deep Learning Research Intern
Huawei Technologies, Noah's Ark Research Lab
May 2019 - May 2020 | Toronto, ON

Research and development for autonomous systems (self-driving technology). Research focus and related topics: 2D/3D semantic scene completion, LiDAR-based segmentation, road estimation, visual odometry, depth estimation, and learning-based localization.

clean-usnob Autonomy Engineer - Object Detection
aUToronto, Object Detection Team | SAE/GM Autodrive Challenge
Aug 2019 - Apr 2020 | Toronto, ON

Developed a state-of-the-art deep learning pipeline for real-time 3D detection and tracking of vehicles, pedestrians and cyclists from multiple sensor input.

clean-usnob Robotics Research Intern
Autonomous Systems and Biomechatronics Lab | Advised by Prof. Goldie Nejat
Department of Mechanical and Industrial Engineering, University of Toronto
May 2018 - Aug 2018 | Toronto, ON

Search and rescue robotics - research on the topics of Deep Reinforcement Learning and Transfer Learning for autonomous robot navigation in rough and hazardous terrain. ROS (Robot Operating System) software development for various mobile robots.

clean-usnob Software Engineering Intern
General Electric, Grid Solutions
May 2017 - Aug 2017 | Markham, ON

Created customer-end software tools used to accelerate the transition/setup process of new protection and control systems upon upgrade. Designed the current Install-Base and Firmware Revision History databases used by GE internal service teams.

Learn by doing - I've had the opportunity to work on many interesting projects that range across industries such as Robotics, Health Care, Finance, Transportation, and Logistics.

Links to the source code are embedded in the project titles.

clean-usnob Instruction Prediction as a Constructive Task for Imitation and Adaptation
Stanford University, CS330 Deep Multi-task and Meta Learning

Can natural language substitute as abstract planning medium for solving long-horizon tasks when obtaining additional demonstrations is prohibitively expensive? We show: (a) policies trained to predict actions and instructions (multi-task) improves performance by 30%; (b) policies can be adapted to novel tasks (meta learning) solely from language instructions. Project report / Poster

clean-usnob Controllable and Image-Free StyleGAN Retraining for Expansive Domain Transfer
Stanford University, CS348i Computer Graphics in the Era of AI

StyleGAN has a remarkable capacity to generate photrealistic images in a controllable manner thanks to its disentangled latent space. However, such architectures can be difficult and costly to train, and domain adaptation methods tend to forego sample diversity and image quality. We prescribe a set of ammendments to StyleGAN-NADA which improve on the pitfalls of text-driven (image-free) domain adaptation of pretrained StyleGANs. Project report / Presentation

clean-usnob Bayesian Temporal Convolutional Networks
University of Toronto, CSC413 Neural Networks and Deep Learning

In this project, we explore the application of variational inference via Bayes by Backprop to the increasingly popular temporal convolutional networks (TCNs) architecture for time series predictive forecasting. Comparisons are made to the effective state-of-the-art in a series of ablation studies. Project report

clean-usnob SfMLearner on Mars
University of Toronto, ROB501 Computer Vision for Robotics

Adapted the SfMLearner framework from Unsupervised Learning of Depth and Ego-Motion from Video to The Canadian Planetary Emulation Terrain Energy-Aware Rover Navigation Dataset (dataset webpage), and evaluated its feasibility for tracking in low-textured martian-like environments from monochrome image sequences. Project report

clean-usnob 3D Shape Reconstruction
University of Toronto, APS360 Applied Fundamentals of Machine Learning

An empirical study of various 3D Convolutional Neural Network architectures for predicting the full voxel geometry of objects given their partial signed distance field encodings (from the ShapeNetCore database). Project report

clean-usnob Autonomous Packing Robot
University of Toronto, AER201 Robot Competition

Designed, built, and programmed a robot that systematically sorts and packs up to 50 pills/minute to assist those suffering from dimentia. An efficient user interface was created to allow a user to input packing instructions. Team placed 3rd/50. Detailed project documentation / Youtube video

clean-usnob Automated Robotic Garbage Collection
Canadian Engineering Competition 2019, Programming Challenge

Based on the robotics Sense-Plan-Act Paradigm, we created an AI program to handle high-level (path planning, goal setting) and low-level (path following, object avoidance, action execution) tasks for an automated waste collection system to be used in fast food restaurants. 4th place Canada. Presentation

clean-usnob Hospital Triage System
Ontario Engineering Competition 2019, Programming Challenge

Developed a machine learning software solution to predict the triage score of emergency patients, allocate available resources to patients, and track key hospital performance metrics to reduce emergency wait times. 1st place Ontario. Presentation / Team photo

clean-usnob Warehouse Logistics Planning
UTEK Engineering Competition 2019, Programming Challenge

Created a logistics planning algorithm that assigned mobile robots to efficiently retrieve warehouse packages. Our solution combined traditional algorithms such as A* Path Planning with heuristic-based clustering. 1st place UofT. Presentation / Team photo

clean-usnob Smart Intersection - Yonge and Dundas
University of Toronto, MIE438 Robot Design

We propose a traffic intersection model which uses computer vision to estimate lane congestion and manage traffic flow accordingly. A mockup of our proposal was fabricated to display the behaviour and features of our system. Detailed report / YouTube video

clean-usnob Insurance Fraud Detection
CIBC Data Studio Hackathon, Programming Challenge

Developed an unsupervised learning system utilizing Gaussian Mixture Models to identify insurance claim anomalies for CIBC.

clean-usnob Solar Array Simulation
Blue Sky Solar Racing, Strategic Planning Team

Created a simulator that ranks the performance of any solar array CAD model by predicting the instantaneous energy generated under various daylight conditions.

clean-usnob Gomoku AI Engine
University of Toronto, Class Competition

Developed an AI program capable of playing Gomoku against both human and virtual opponents. The software's decision making process is determined by experimentally tuned heuristics which were designed to emulate that of a human opponent.

clean-usnob Word Pairing - Semantic Similarity
University of Toronto, Class Competition

Programmed an intelligent system that approximates the semantic similarity between any two pair of words by parsing data from large novels and computing cosine similarities and Euclidean spaces between vector descriptors of each word.