Shubham Kamble – Portfolio

About Me

I am a Robotics Controls Engineer with over four years of academic research, teaching and professional experience in real‑time robotics software, optimisation and machine learning. My work focuses on transitioning advanced algorithms from simulation to physical deployment, building ML/RL pipelines and developing control frameworks for complex multi‑body robots. With strong mathematical foundations in system identification, optimal control and reinforcement learning, I strive to bridge the gap between theoretical research and real‑world robotics applications.

Experience

Sept 2024 – Present

Robotics Controls Engineer

Neura Robotics GmbH, Metzingen, Germany

Designed and implemented a dynamic identification framework for torque constants, rotor inertia, friction and inertial parameters. Developed Fourier‑series‑based trajectory generation under Riemannian manifold constraints. Created analytical inverse and forward kinematics solvers for humanoid robots. Deployed advanced friction compensation algorithms and optimised genetic algorithm routines. Spearheaded the developement of a generalized inverse kinematics solver.

Nov 2021 – Present

Student Research Assistant

Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen

Built ML pipelines (RNEA+FCNN, LNN) for structured learning of 6‑DOF industrial robot dynamics. Integrated a Doosan Cobot M1013 with hardware peripherals on ROS for reinforcement‑learning‑based assembly automation. Developed a SIMULINK inverse dynamics model for friction identification. Designed a novel inertial parameter identification routine using low‑velocity excitation trajectories. Created explainable AI models using Transformers and Captum for torque prediction interpretability. Deployed research notebooks on RWTH JupyterHub with Docker.

Oct 2022 – Mar 2024

Student Assistant – Optimal Control & Technical Informatics Lab

Chair of Intelligent Control Systems, RWTH Aachen

Guided students in implementing control algorithms (PI, PID, MPC) on FloatShield apparatus using Arduino; Prepared lecture notes on the Pontryagin Maximum Principle. Graded and evaluated coursework for over 50 students. Supported laboratory sessions on Kalman Filters, Model Predictive Control and Receding Horizon Control.

Aug 2018 – Aug 2019

Product R&D Engineer (Mechatronic Systems)

Carnot Technologies, Mumbai, India

Conceptualised and prototyped automation systems. Developed functional prototypes aligned with ISO/IEC standards. Generated Bills of Materials to streamline sourcing.

Education

Oct 2020 – May 2024

M.Sc. in Robotic Systems Engineering

RWTH Aachen University, Germany

Optimal Control, Reinforcement Learning, Numerical Optimisation, Self‑driving Vehicles, Machine Learning, Computer Vision and Advanced robot Kinematics/Dynamics.

Aug 2014 – May 2018

B.Tech. in Mechanical Engineering

Veermata Jijabai Technological Institute (VJTI), Mumbai, India

Mechatronics, Automotive Engineering, Electrical Drives, Computational Fluid Dynamics and Finite Element Methods.

Skills

C++ & Python

High‑performance robotics, control algorithms and reinforcement learning development.

Control & Optimisation

Advanced robot Kinematics & Dynamics, Optimal Control, and Trajectory Optimisation techniques.

State Estimation & Bayesian Optimisation

Advanced state estimation (Kalman, Extended-Kalman, Unscented Kalman, and advanced Particle filtering), and Bayesian Optimisation techniques.

Machine Learning & Reinforcement LearningL

Building ML pipelines and applying reinforcement learning to robotics.

Frameworks & Tools

Linux, Docker, Git, Gitlab, ROS2, JAX, PyTorch, MATLAB, SIMULINK.

System Identification

Inertial parameters, rotor inertia, friction parameters, and elastic model estimation.

Soft Skills

Independent learner, critical thinker, problem solver and good communicator.

Projects

Advanced Inverse Kinematics

Developed an iterative and redundancy‑resolving inverse kinematics solver using the pseudo‑inverse Jacobian, null‑space projection and secondary objectives.

Learn more

Regularization in Differential Dynamic Programming

Master Thesis: Regularization in DDP

Improved regularisation methods for Differential Dynamic Programming, achieving faster convergence and stability with applications in autonomous driving.

Research Project Thesis: Condition Number Optimisation

Optimized excitation trajectories to minimize condition number of a simplified regressor matrices based on specially designed trajectories.

Learn more

Seminar: Differential Dynamic Programming

Analyzed the paper “Synthesis and stabilization of complex behaviors through online trajectory optimization” and presented key takeaways in trajectory optimization.

Learn more

Seminar: Review on the AlphaGo RL algorithm

Analyzed DeepMind’s AlphaGo architecture; presented insights on Monte Carlo Tree Search + Deep RL.

Learn more

Seminar: Ethics in AI and Robotics

Analysed and presented research on online trajectory optimisation, Differential Dynamic Programming with regularisation and learning‑based control methods including AlphaGo and deep reinforcement learning.

Learn more

Blogs

Contact

If you’d like to get in touch and seek my expertise, then feel free to get touch via the following platforms. I’m open to collaborations, mentorship and interesting projects.

GitHub