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Sunny Deshpande
Robotics Engineer

Sunny Nitin
Deshpande

Shipping autonomous systems from simulation to the real world — production fleet deployment (200+ AMRs), real-vehicle autonomy, and sim-to-real RL for humanoid robots. MEng in Autonomy & Robotics at UIUC.

sunnydeshpande9900@gmail.com
Selected Projects

Real-world deployments, novel algorithms, and sim-to-real systems across humanoids, AVs, and mobile robots. Featured projects showcase the deepest work.

C.A.R.E. Robot👓
CalHacks 12.0

C.A.R.E. — Companion Autonomous Robotic Entity

Full-stack embodied AI on physical Booster K1 Humanoid: Gemini VLM semantic goal planning with ROS2 Nav2 autonomous navigation and Snap AR Spectacles AR collaboration.

ROS2Gemini VLMNav2OpenCVWebSocketAR
Oct 2025 - Oct 2025
Lane Tracking Race Car🏁
SUTD Coursework

Lane-Tracking 4WD Race Car

Autonomous lane-following on Jetson Xavier + ZED2: dual-filter perception (Gaussian-weighted adaptive grayscale + HLS color space) with bitwise AND fusion targeting 8% pixel density, vanishing-point radial-scan histogram rejecting racetrack markings, and sliding-window 2nd-order polynomial fit. Custom proportionate lateral controller with distance-weighted steering (f(c_y) = C/(c_y − h_y + ε)). Completed full 12-min autonomous laps across variable lighting conditions.

Jetson XavierZED2OpenCVLane DetectionAdaptive Thresholding45+ FPS
Jan 2025 – Apr 2025
Maze SLAM🗺️
SUTD Coursework

Maze Frontier Exploration SLAM + Object Tracking

Autonomous unknown-maze exploration on TurtleBot3 (LattePanda): frontier-based exploration with DFS cluster grouping and centroid-based goal selection, A* costmap planner with B-spline path smoothing, Pure Pursuit trajectory follower, and fine-tuned YOLOv11n with a novel spatial deduplication tracker projecting bounding-box centroids into the map frame via LiDAR range lookup for unique instance counting.

SLAM ToolboxA*Pure PursuitFrontier ExplorationYOLOv11nROS2
Sep 2024 – Dec 2024
Autonomous Underwater Vehicle🐟
SOAR · Software Lead

SUTD Autonomous Underwater Vehicle

Software Lead. Cascaded PID for 6-DOF underwater stability with IMU/depth fusion via state-space control. Real-time CV for autonomous target acquisition. SAUVC 2023 Finalist.

PIDState-SpaceIMU FusionComputer VisionOpenCV
Sep 2022 – Apr 2024
Visual Odometry in Rain🌧️
MARVL Lab · Published

Deep-Learning VO for Autonomous Driving in Rain

Systematic evaluation of 7 monocular and stereo VO algorithms (DSO, SVO, CNN-SVO, DF-VO, TartanVO, ORB-SLAM3, DROID-SLAM + our heuristic variant) across 3 geographically-diverse rain datasets (Oxford RobotCar, 4Seasons Munich, Singapore heavy rain) using Absolute Trajectory Error. DF-VO performed best for monocular <500 m; our DROID-SLAM + CGRP + Heuristic approach achieved the lowest stereo ATE for long-range localization. Published at IEEE CASE 2023.

DROID-SLAMORB-SLAM3DF-VOVisual OdometryStereo VisionPyTorch
May 2023 – Aug 2023
Bio-Inspired Underwater Robot🐠
BRD Lab

Bio-Inspired Underwater Robot Control

CPG-based motion controller for soft batoid robot with sinusoidal propulsion and strain gauge obstacle detection.

CPGSoft RoboticsSignal ProcessingDSP
Jan 2022 – Apr 2022
Lightning Risk Prediction⛈️
SUTD · Deep Learning

Category 1 Lightning Risk Prediction using CNN-RNN

CNN-RNN model predicting Cat 1 Lightning Risk from weather station features: rainfall, wind speed, temperature, humidity, wind direction.

CNN-RNNTime SeriesPyTorchWeather
Jan 2025 - Apr 2025
Sentiment Analysis🎬
SUTD · NLP

Sentiment Analysis — BiLSTM-DistilBERT

Contextual transformer embeddings + sequential modeling. ~96% accuracy outperforming classical NLP baselines.

DistilBERTBiLSTMNLPPyTorch
Jan 2024 – Apr 2024
HMM Sequence Labelling📊
SUTD · ML

Automated Sequence Labelling using HMM

Hidden Markov Models with first- and second-order Viterbi decoding for entity recognition and sentiment tagging.

HMMViterbiNERPython
Jan 2024 – Apr 2024
Epilepsy EEG Diagnosis🧬
NP Research · Published

Focal EEG Signal Characterization for Epilepsy Diagnosis

Extracted and benchmarked 9 nonlinear feature families (MMSE, RQA, DFA, entropies, FD, Hjorth, Hurst, LLE, LZC) from 7,500 focal/non-focal EEG signals (Bern-Barcelona database). LS-SVM (polynomial-3) with 10-fold CV achieved 87.93% accuracy, 89.97% sensitivity. MMSE ranked highest by t-test significance. Published in FGCS, Elsevier.

LS-SVMEEGNonlinear FeaturesMMSERQAMATLAB
Mar 2018 – Aug 2019
Pipe Leak Detector🔌
SUTD · EM

Variable-Length Fluid Pipe Leak Detector

Low-cost electromagnetic leak detector with Bluetooth connectivity using EM conduction principles for non-invasive detection.

EM DesignBluetoothAntennaFabrication
May 2023 – Aug 2023
TROLL-E Trolley🛒
SUTD · EDI

TROLL-E: Smart Foldable Electric Grocery Trolley

Custom PCB with motor control, dynamic air-cooling, slope-based actuation, and anti-roll basket mechanism.

PCB DesignMotor ControlKiCADSolidWorks
Jan 2023 – Apr 2023
Quad-Rotor Helicopter🚁
Personal Build

Teleoperable Quad-Rotor Helicopter

Low-cost quadcopter with off-the-shelf components. Integrated propulsion, power distribution, and flight electronics for stable teleoperation.

ElectronicsFlight ControlPID Tuning
Jan 2023 - Jan 2023
Nature-Inspired Aerial Craft🚁
AIR Lab

Nature-Inspired Aerial Robotic Craft

Angsana seed-inspired autorotating craft with single-flap motor trajectory control and aerodynamic calibration.

AerodynamicsFabricationCADMATLAB
Sep 2021 – Dec 2021
8-Bar Lifting Robot🏗️
Asia-Pacific Vex Robotics 2014

8-Bar Cube Frame Lifting Robot

8-bar lifting mechanism with novel intake for cube frame collection. Won Best Design Award at Asia-Pacific Robotics Championship.

Mechanism DesignCompetitionFabrication
Jan 2014 - Aug 2014
Work Experience

Production deployments, real-vehicle autonomy, and simulation at scale — from 200+ AMR fleets to port-side AV trials.

May 2024 – Sep 2024

Hyundai Motor Group Innovation Centre

Robotics Fleet Software Engineer Intern · Robotics Center
  • Overhauled fleet communication for 200+ production logistics AMRs from polling-based REST to event-driven MQTT (QoS-1), cutting command-response latency from 332 ms to 151 ms and enabling real-time telemetry across fleet.
  • Replaced stop-and-wait intersection coordination with velocity-profiled trajectory blending (Bezier smoothing, adaptive-lookahead pure pursuit), increasing multi-robot intersection throughput by 40% via staged rollout to 200-AMR fleet.
  • Designed traffic-aware fleet path planner with corridor congestion scoring, ETA-based rerouting, and kinematic feasibility validation for constrained warehouse aisles; increased completed missions by 10% via staged canary deployment.
  • Performed fleet failure analysis via Gazebo log replay of failed missions, diagnosing trajectory tracking and coordination faults; findings fed directly into planner parameter tuning and intersection logic refinements.
ROS2MQTTREST APIMotion PlanningPath PlanningTrajectory BlendingBezierPure PursuitFleet ManagementGazeboPython200+ AMRs
Sep 2023 – Dec 2023

Venti Technologies

AV Simulation Engineer Intern · Planning and Control Team
  • Extended VTD physics simulation from single-trailer to multi-trailer articulated ego vehicles (Autonomous Prime Movers), computing real-time hitch angles, per-trailer base_link velocity/acceleration, and inter-trailer distances via custom ROS messages and topics; modularized the dynamics/kinematics pipeline into base and extended OOP class hierarchy so adding trailers required zero additional code.
  • Built an automated brake performance testing framework executing parameterized scenarios (braking speed, deceleration percentage) to calibrate and validate simulated vehicle dynamics; diagnosed and fixed steering deviation from planned trajectory by adjusting the steering factor, and investigated RPS/gear-shifting discrepancies with the vendor's physics model.
  • Refactored the simulation codebase to toggle image processors based on Docker instance type; separated dynamics extraction into configurable feature toggles for lean versus full-fidelity simulation runs.
  • Participated in company-wide fleet trials at port: performed software deployment on APMs, triaged operational logs, classified bug tickets, and raised issues requiring further investigation — pipeline adopted post-internship for regression testing.
VTDMPCVehicle DynamicsMulti-Trailer ArticulationROSSimulationDockerC++PythonJiraRegression TestingAPM Fleet
May 2019 – Aug 2019

A*STAR (I2R)

Robotics & AI Research Intern · Perception Team
  • Designed an end-to-end CNN visual navigator on a Pioneer P3-DX: the network takes synchronized 4-channel RGB-D images (Astra Mini) and a pose-difference vector (odometry → nearest local path pose) as inputs, and directly outputs Twist motor commands — replacing the move_base planner's control loop with a single learned regression model. Trained 50+ CNN architectures, selecting models with <0.04 loss for live testing.
  • Built a custom ROS data pipeline automating the full collection→training workflow: random/patrolling waypoint generators cycling goals indefinitely, a Twist timestamper (custom node) enabling temporal synchronization, and an approximate-time message filter aligning RGB-D frames, odometry, local path, and cmd_vel at ~2.5 Hz — producing 36,520 synchronized training samples with zero manual labeling (move_base generates ground-truth commands).
  • Achieved an 80× costmap update acceleration (0.125 Hz → 10 Hz) by inserting a voxel grid downsampler and statistical outlier removal between the Astra Mini pointcloud and move_base's local costmap, enabling real-time obstacle avoidance of sub-LiDAR and overhanging obstacles invisible to the 2D Hokuyo UTM-30LX laser scan.
  • Evaluated two camera configurations (horizontal: long-range but floor blind-spot; angled: 0.8 m higher, tilted down for near-field coverage); the angled setup significantly outperformed horizontal in cluttered AV Lab trials by eliminating the sub-camera blind zone.
CNNRGB-DROSTensorFlow / KerasGMapping SLAMAMCLmove_baseElastic Band PlannerVoxel GridObstacle Avoidance36K+ SamplesPythonPioneer P3-DX
Technical Skills

Full-stack robotics — from perception and planning to learning and deployment.

Autonomy and Robot Learning

Robot Learning 14

Deep LearningReinforcement LearningPPOSACH-MARLVision-Language-Action (VLA)Behavior CloningDAggerImitation LearningCurriculum LearningDomain RandomizationSim-to-Real TransferNLPTransformers

Perception & SLAM 12

Computer VisionSLAMVisual Odometry6D Pose EstimationObject DetectionSAM-3YOLOv11Semantic SegmentationInstance SegmentationLiDARStereo VisionRGB-D

Autonomy & Controls 13

Motion PlanningPath PlanningTrajectory OptimizationMPCPIDState SpacePure PursuitStanley ControllerAdmittance ControlSensor FusionKalman FilterBehavior TreesFinite State Machine

Spoken Languages: English (Fluent) · Hindi (Fluent) · Marathi (Fluent)

Academic Background

A focused academic path through engineering, robotics, and CS.

Aug 2025 – Sep 2026

University of Illinois Urbana-Champaign

MEng in Autonomy and Robotics · GPA 3.75/4.00
Reinforcement Learning, Advanced Computer Vision, Autonomous Vehicle Safe Autonomy, Humanoid Robotics
Sep 2021 – May 2025

Singapore University of Technology and Design

BEng (Engineering Product Development), Robotics Focus · Minor in CS
SUTD Honours and Research Programme (SHARP) · Global Distinguished Scholarship
May 2016 – May 2019

Ngee Ann Polytechnic

Diploma in Engineering Science
Specialization in Automation and Mechatronics Systems · NP Engineering Merit Awardee
Jan 2012 – Dec 2016

School of Science and Technology, Singapore

GCE O'Levels
Pure Physics and Chemistry · Fundamentals of Electronics (Applied Subject)
Publications

Peer-reviewed contributions to robotics and biomedical AI.

19th IEEE International Conference on Automation Science and Engineering (CASE 2023)

Evaluating Visual Odometry Methods for Autonomous Driving in Rain

Comprehensive evaluation of 7 VO algorithms (DSO, SVO, CNN-SVO, DF-VO, TartanVO, ORB-SLAM3, DROID-SLAM) on monocular and stereo setups across Oxford RobotCar, 4Seasons (Munich), and Singapore heavy-rain datasets. Proposed DROID-SLAM + CGRP + Heuristic variant achieving lowest stereo ATE for long-range rain localization; DF-VO identified as best monocular approach for <500 m.

Download Paper
Future Generation Computer Systems (FGCS), Elsevier, 2019

Characterization of Focal EEG Signals: A Review

First systematic comparison of 9 nonlinear feature families (52 features, all p < 0.01) for focal vs. non-focal EEG classification on the full 7,500-signal Bern-Barcelona database. LS-SVM (polynomial-3, 10-fold CV) achieved 87.93% accuracy / 89.97% sensitivity. MMSE identified as top-ranked discriminator; proposed recurrence, bispectrum, and cumulant plots for visual class separation. DOI: 10.1016/j.future.2018.08.044

Download Paper
Role-Specific Resumes

Tailored resumes highlighting relevant experience for each role.

Awards & Competitions
1st

Deep Dive Singapore

Winning Business Pitch · 2025
Final

SAUVC

Autonomous Underwater Vehicle · 2024
3rd

Build On Singapore Hackathon

Category B · 2019
Merit

NP Engineering Merit Award

Academic Excellence · 2017
Best

NJRC Best Programming + Mech Design

National Junior Robotics · 2016–17
Best

Asia-Pacific Robotics Design + 3rd

Championship · 2014
1st

FIRST Tech Challenge

Winning Alliance · 2013
3rd

Vex Robotics Best Programming

Singapore · 2013