Pramod Pal I am PhD student in IISc Bangalore
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Hello, I'm a graduate student specializing in Mechanical design, Robotics, and Artificial Intelligence. Currently, I'm working towards my PhD in Robotics at one of India's top institutions. My strengths include Mathematics, Physics, Robotics, Dynamics and Control, Reinforcement Learning, Deep Learning, Python, MATLAB, Mujoco simulation, gym environment (gymnasium), Pytorch, wheeled robot simulator, Programming, and ROS, I have also used the simulator platform like(Webot, MujoCo, Pybullet, Isac gym, Robotarium, Coppelia Simulator ).

When it comes to teaching, my approach is somewhat unique. I emphasize building a strong foundation in core concepts and practicing them regularly. Understanding the theory is crucial, but practical application reinforces these concepts and makes them stick. With me, you'll engage in plenty of hands-on activities, solving problems and applying solutions. This method ensures that you not only grasp the concepts but also understand their real-world applications, helping you remember them better.

Till now I have taught Robotics (3 students (1 continuing included), Dynamics and Control(2 Students), Solid Mechanics (2 students), and Reinforcement Learning (3 students (1 continuing included)). Additionally, I've had the opportunity to serve as a Teaching Assistant for NPTEL courses in Robotics and Dynamics and Control, each for three times over three years. These courses, led by Professor Ashitava Ghosal, are part of an initiative started in 2003 by MHRD, along with 7 IITs and IISc Bangalore. This initiative aims to offer quality education accessible to anyone interested in learning from the IITs.

Regarding assignment help, I have helped different students with between 80 to 100 assignments on time over the past three years. These assignments covered various topics, including Robotics, Solid Mechanics, Dynamics and Control, Mathematics, Reinforcement Learning, ROS, MATLAB, Mujoco simulation, wheeled robot simulator, gym environment (gymnasium), Pytorch, Programming, and Machine Learning.

For live assignment assistance, I've supported numerous students in achieving high marks, with more than 85% of them scoring above 80%. The areas of help included Robotics, Solid Mechanics, Dynamics and Control, Mathematics, Reinforcement Learning, MATLAB, Mujoco simulation, wheeled robot simulator, Programming, ROS, and Machine Learning.

Subjects

  • Solid Mechanics Expert

  • Machine Learning Expert

  • Reinforcement Learning Expert

  • Artificial Intelligence Deep Learning Expert

  • Dynamics of Mechanical systems Expert

  • Deep Learning Projects in Python Expert

  • Robotics and Control Expert

  • MATLAB & Simulink Expert

  • Mujoco Rigid body simulator Expert

  • open ai gym Expert

Experience

  • Teacher (Jun, 2020–Present) at Self-employed, engaged in Online Tutoring
    I have successfully completed a course in Reinforcement Learning, Linear Algebra, Differential Equations, Dynamics and control, Robotics and in Solid mechanics,

Education

  • PhD in Mechanical engineering (Jan, 2019–now) from Indian institute of science, Bengaluru

Fee details

    ₹2,000–5,000/hour (US$23.78–59.46/hour)

    The fee depends upon the students requirement and the course he wants to study. It's mutual to decide the fee.

Courses offered

  • Reinforcement learning

    • USDFREE
    • Duration: 35 Hours
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English
    • Certificate provided: No
    Week 1 : Introduction
    Week 2 : Bandit algorithms – UCB, PAC
    Week 3 : Bandit algorithms –Median Elimination, Policy Gradient
    Week 4 : Full RL & MDPs
    Week 5 : Bellman Optimality
    Week 6 : Dynamic Programming & TD Methods
    Week 7 : Eligibility Traces
    Week 8 : Function Approximation
    Week 9 : Least Squares Methods
    Week 10 : Fitted Q, DQN & Policy Gradient for Full RL
    Week 11 : Hierarchical RL
    Week 12 : POMDPs

  • Robotics

    • USDFREE
    • Duration: 35 Hours
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English
    • Certificate provided: No
    Week 1 Introduction to robotics- History, growth; Robot applications- Manufacturing industry,
    defense, rehabilitation, medical, etc., Laws of Robotics
    Week 2 Robot mechanisms; Kinematics- coordinate transformations, DH parameters
    Week 3 Forward Kinematics, Inverse Kinematics
    Week 4 Jacobians, Statics, Trajectory Planning
    Week 5 Actuators (electrical)- DC motors, BLDC servo motors
    Week 6 Sensors, sensor integration
    Week 7 Control – PWM, joint motion control, feedback control
    Week 8 Computed torque control
    Week 9 Perception, Localisation and mapping
    Week 10 Probabilistic robotics, Path planning, BFS; DFS; Dijkstra; A-star; D-star; Voronoi; Potential Field;
    Hybrid approach
    Week 11 Simultaneous Localization and Mapping
    Week 12 Introduction to Reinforcement Learning

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