Emad Razavi
Robotics Engineer (MSc) focused on perception and navigation research for mobile robots, with a background in electronics, embedded systems, and control theory, and hands-on robotics system development experience.
My path into robotics started from electrical engineering, where I built a foundation in electronics,
embedded systems, control theory, and optimization. That background shaped the way I approach robots:
not as isolated algorithms, but as complete systems where sensing, computation, actuation, and reliability
have to work together.
During my robotics studies, I moved deeper into perception and navigation for mobile robots, especially
systems that can leave simulation and operate on real platforms. My work has included semantic mapping,
object-goal navigation, ROS 2 integration, Nav2-based autonomy, and experiments with physical robots.
I am especially interested in research and engineering that connects perception with action: robots that
understand enough about their environment to move safely, make useful decisions, and support people in
real-world tasks.
My academic background combines robotics engineering, electrical engineering, embedded systems, control
theory, and optimization.
MSc in Robotics Engineering
University of Genoa, 2023-2025.
Thesis: Semantic Object-Goal Navigation on a Quadruped Robot in Known Environments
Open master thesis explanation
BSc in Electrical Engineering
Qazvin Azad University, 2018-2023.
Thesis: Optimal PID Controller Tuning for Time-Delayed Systems Using Ant Colony Optimization
Open bachelor thesis explanation
This thesis developed a two-stage object-goal navigation pipeline for Boston Dynamics Spot. The system first
records a clean 2D map and a compact set of confirmed object instances, then reuses that saved world model
to navigate to named objects safely and repeatably.
Stage 1: Pre run mapping
A short teleoperation run builds the occupancy map and records only stable RGB D object observations into a semantic database.
Stage 2: Object goal navigation
Spot localizes on the saved map, selects a requested object, and sends Nav2 a safe standoff goal near that object. Targets can be selected from a CLI or by voice.
Work carried out at Dynamic Legged Systems,
Istituto Italiano di Tecnologia.
System overview
The stack is intentionally lightweight: Spot uses a 2D LiDAR for mapping/localization, a RealSense T265
for visual-inertial odometry, and a RealSense D435 for RGB D detections. SLAM Toolbox builds the pre-run
map; during navigation, AMCL localizes on the fixed map and Nav2 sends goals through the ROS 2 bridge.
- Two-stage design: navigation relies on a fixed map and a recorded semantic database instead of rebuilding the world online.
- Confirmed objects only: detections are saved after repeated support over time, with gating and duplicate merging.
- Inspectable behavior: the map, object list, selected target, generated goal, and Nav2 plans can be checked in RViz.
Demo 1: Semantic mapping
During the pre run, D435 RGB detections are projected into 3D using depth and transformed into the map
frame. The semantic layer keeps temporary observations separate from confirmed objects, which reduces noisy
entries in the final database.
- Proposal memory: temporary observations that must survive repeated checks.
- Static memory: confirmed object instances, merged when nearby detections describe the same object.
- Recorder output: a human readable YAML database used later for object-goal navigation.
Demo 2: Voice based object goal navigation
In the second stage, Spot loads the saved map and object database, then receives a target request by voice.
Whisper transcribes the command, embedding matching selects the closest recorded object instance, and the
object pose is converted into a safe standoff goal for Nav2.
- Goal generation: targets are not placed inside objects; they are offset with a clear facing direction.
- Execution: Nav2 receives a standard NavigateToPose goal with map, LiDAR obstacle, and inflation costmaps.
- Safety: Spot's native safety layer remains active under the ROS 2 navigation stack.
Implementation and evaluation
The implemented system includes a confirmed-only semantic layer, an object-to-goal interface for Nav2,
onboard Spot deployment, RViz/rosbag tooling, and repeated-run supervision. Evaluation covered three real
indoor environments: a church, the DLS lab, and a large IIT test room.
The same pipeline produced usable 2D maps across different layouts and kept the semantic database compact
by focusing on a small set of object classes. The main observed limitations were depth alignment bias in
object placement and localization inconsistency causing early goal acceptance in edge cases. Both failure
modes were visible in logs and RViz, making the system inspectable and technically actionable.
Back to Education
Optimal PID Controller Tuning for Time-Delayed Systems Using Ant Colony Optimization
This page is ready for the detailed explanation of the BSc thesis.
Back to Education
My experience sits at the intersection of mobile robotics, perception, semantic mapping, autonomous
navigation, and practical robotics system development.
IIT Dynamic Legged Systems
Semantic object-goal navigation on quadruped robots, combining perception, mapping, and Nav2-based execution on physical platforms.
MRL Robotics Team
Team-based robotics research and development in RoboCup Small Size League, including competitive system integration and documentation.
DOPE Aerospace
Applied robotics and engineering collaboration through the University of Genoa ecosystem.
Industry Projects
Engineering experience through Radman System and ORBI, with practical implementation work beyond academic projects.
Semantic Navigation
ACO PID Tuning
Construction Robotics
Semantic Object-Goal Navigation on a Quadruped Robot in Known Environments 2025
Open publication
My master's thesis presents a practical object-goal navigation pipeline for Boston Dynamics Spot in real
indoor environments. A pre-run records a 2D map and semantic object database; during operation, the robot
localizes on the saved map, selects a recorded object target, and navigates using a layered Nav2 costmap
with onboard sensing and computation.
Online Object-Level Semantic Mapping for Quadrupeds in Real-World Environments 2025
Open publication
This paper describes an online semantic object mapping system for quadruped robots. The mapper combines
range geometry with RGB D detections, merges repeated observations into persistent object instances, and
produces a compact object layer that can be queried by class, pose, and confidence.
MRL Extended Team Description 2020 2020
Open publication
This team description paper presents the MRL Small Size League robot hardware and software design,
including rule-driven software updates, reliability improvements, accuracy enhancements, and changes to
the electronic structure that enabled more complex robot behavior.
I am open to PhD, research, and robotics engineering opportunities involving autonomy, perception,
navigation, SLAM, and real-world robot deployment.
