I'm a Master's student in Electrical Engineering (Smart Systems) at the University of Stuttgart, passionate about developing intelligent robotic systems that bridge the gap between simulation and real-world applications.

I'm currently looking for entry-level roles in robotics and autonomous systems, with a focus on robotic manipulation, deep reinforcement learning, and simulation-to-real transfer in industrial or research environments.

• Master's Thesis at Fraunhofer IPA: Developing digital twin-driven autonomous assembly systems for snap couplings using deep reinforcement learning in NVIDIA Isaac Lab with UR5e manipulator and 6-DOF,12-joints dexterous Inspire hand
• Building universal grasping frameworks with sim-to-real transfer capabilities

Left: Cube | Right: Cylinder

© 2026 Apurv Patel | This work is part of my Master's thesis at University of Stuttgart and Fraunhofer IPA.

• MSc Electrical Engineering - Smart Systems | University of Stuttgart (2022-Present)
• B.Tech Electrical Engineering - Power Electronics | Pandit Deendayal Energy University (2016-2020)

• Research Assistant @ Fraunhofer IPA (Sep 2023 - May 2025)
  • Implemented NVIDIA Isaac Lab environments for robotic manipulation data generation
  • Designed novel 3-finger adaptive gripper with computer vision integration
  • Developed ROS/Gazebo simulation frameworks for robotic risk assessment
• Commissioning Engineer @ JK Cement Ltd (Dec 2020 - Dec 2021)
• Team Lead - Electrical Design @ Team Kaizen (Jun 2018 - May 2019)

Robotics & AI

• ROS/ROS2, NVIDIA Isaac Sim/Lab, RSL-RL
• Deep Reinforcement Learning, Computer Vision (OpenCV)
• MoveIt, Gazebo, CoppeliaSim

Programming & Tools

• Python, C/C++
• MATLAB/Simulink
• SolidWorks, Altium Designer, LTspice

Specialized

• Robotic Manipulation, Control Systems
• Machine Learning Pipelines
• Hardware Integration & Testing

• "A Time-series Data Generation Tool for Risk Assessment of Robotic Applications" - ESREL/SRA-E 2025 Conference
• "Operational Amplifier Based dc-ac Converter for Domestic Application" - ICOEI-2020 Conference

Comprehensive simulation framework for Franka Panda manipulator with fault injection mechanisms and PyQt5-based control interface for robotic risk assessment

Novel gripper with variable triangle configurations, integrated computer vision pipeline, and physics-based simulation validation

Data generation pipeline using UR10 manipulator for graph-based ML models (AdaptiGraph framework)

• LinkedIn: linkedin.com/in/apurv-patel-891475167
• Email: [email protected]
• Location: Stuttgart, Germany

Autonomous manipulation • Deep reinforcement learning • Sim-to-real transfer • Robot control systems • Real-world robotic applications