SYSTEM ONLINE // URBANA, IL

AUTONOMY
ARCHITECT

> CURRENT_TASK: Master's_Robotics_UIUC

> FOCUS: Humanoids, Safe Autonomy, Embedded AI

> STATUS: Developing Intelligent Systems

01

ACTIVE_RESEARCH // FALL_2025

Safe Autonomy (GEM Vehicle)

Stanley Controller | Pedestrian Prediction | ROS2 | YOLO

Developing pedestrian-behavior-based control policies for the GEM autonomous vehicle. Focusing on predictive braking and path planning in high-uncertainty environments using behavioral models.

Dynamic Human-Robot Handover

HRI | Motion Tracking | Real-time Control

Building a closed-loop system where a robot tracks a human hand to dynamically hand over objects. Instead of the human reaching out, the robot adapts to the human's position for a seamless, stable transfer.

6D Pose Estimation

PyTorch | Model-Free Algos | RGB-D

Implementing and comparing model-based vs. model-free algorithms for estimating the 6D pose (position + orientation) of texture-less industrial objects in cluttered scenes.

02

SYSTEM_SPECIFICATIONS

AUTONOMY_STACK

ROS 2 / Nav2 SLAM (VINS-Fusion, ORB-SLAM3) Motion Planning (MoveIt, OMPL, RRT*) Sensor Fusion (Kalman Filters) Control Barrier Functions (CBF)

AI_&_PERCEPTION

Computer Vision (OpenCV, YOLO) Deep Learning (PyTorch, TensorFlow) Reinforcement Learning (PPO) 6D Pose Estimation Point Cloud Processing

EMBEDDED_&_HW

Embedded C / C++ / Rust STM32 / ESP32 Firmware PCB Design (KiCad, Altium) Comms (I2C, SPI, UART, CAN, MAVLink) Fusion 360 / SolidWorks
03

MISSION_LOGS

RUNTIME_HISTORY (WORK)

May 2024 - May 2025

Electronics Engineering Intern

Dimension Six Technologies, Mumbai
  • Enhanced STM32 firmware with a novel power management algorithm, increasing e-bike range by 40%.
  • Designed and routed a 4-layer PCB in KiCad for a custom ESC, reducing power losses by 15% under peak load.
  • Deployed an IoT solution using ESP32S3 and RFID for remote monitoring and secure automated payments.
Jan 2024 - Jun 2024

Robotics Research Intern

IIT Bombay
  • Developed SLAM-based autonomous robot in ROS2, achieving 95% navigation accuracy in dynamic environments.
  • Improved localization by 20% via IMU, GPS, and RGB-D camera sensor fusion.
  • Trained and integrated a YOLOv3 model for real-time human detection and robust stair-climbing navigation.

KERNEL_VERSIONS (EDUCATION)

Aug 2025 - Present

Master's in Autonomy and Robotics

University of Illinois Urbana-Champaign
  • Coursework: Humanoid Robots, Computer Vision, Safe Autonomy, Control Systems, Reinforcement Learning
  • Key Lab Work: Working with humanoids, drones and GEM autonomous platforms.
Dec 2021 - Jun 2025

B.Tech. in Electronics & Telecom

Sardar Patel Institute of Technology
  • Specialization in Embedded Systems and Robotics.
  • Minors in Management (S.P. Jain Institute).
04

ARCHIVED_MODULES (PAST PROJECTS)

Quadruped Locomotion (RL)

PPOIsaac GymPython

Trained ANYmal quadruped policies using Proximal Policy Optimization. Achieved robust traversal on irregular terrain with 0-fall safety constraints.

VIO + Footstep Planning

ROS2VINS-FusionNav2

Fused Visual-Inertial Odometry with footstep planning. Enabled autonomous navigation in GPS-denied environments with <10cm drift.

Humanoid Motion Planning

DrakeMoveItC++

Implemented whole-body motion planning for HRP-4. Enforced ZMP constraints for stable manipulation and reaching tasks.

Terrain-Aware Navigation

Elevation MappingROS2

Developed a perception pipeline converting depth clouds to elevation maps for quadruped footstep placement.

EMG Prosthetic Arm

ESP32TinyMLFusion 360

Built a 5-DOF prosthetic arm controlled by EMG signals. Used XGBoost on ESP32 for real-time gesture classification.

RL Locomotion + Safety

Control Barrier Functions

Integrated CBF as a safety filter for RL policies to guarantee stability during training.

05

VISION_PROTOCOL

manifesto.md

> # THE OBJECTIVE

The robotics industry is building calculators. I want to build the smartphone.


> # THE CONCEPT

"One Robot. Many Lives."

My vision is a Universal Chassis augmented by a Skill Ecosystem. Instead of single-purpose hardware, we need a core intelligence that can download "Physical Apps" - mastered skills for logistics, caregiving, or disaster response.


> # THE IMPLEMENTATION

The key is not just mechanics, but the Core Intelligence - an OS that translates human intent into flawless execution using verified skill blocks. This system must be built on a foundation of "Simulation-First" safety protocols.

06

ESTABLISH_UPLINK

EMAIL_TRANSMISSION LINKEDIN_NETWORK GITHUB_REPO