Introduction

This blog is meant for students interested in robotics, mechanical design, control systems, and how techniques like machine learning and reinforcement learning fit into these fields. The insights come directly from a conversation with Mr. Hercules, who completed his Master’s from IIT Bhubaneswar in Mechanical System Design and is currently pursuing his Ph.D. at IISc Bangalore in robotics. IISc Bangalore is one of India’s top institutes. During the conversation, he explained his work on controlling a quadruped (four-legged) robot, talked about the subjects and tools students should learn, discussed different types of robotic projects, showed how reinforcement learning is applied, and described the opportunities that exist in the industry. Throughout the discussion, he stressed understanding the fundamentals before moving to advanced concepts.

Mr. Hercules’s Background and Current Work

Mr. Hercules is focusing on the control part of a quadruped robot. A quadruped is a robot with four legs, and controlling it is quite difficult compared to controlling a wheeled robot (often called a UGV—Unmanned Ground Vehicle). He explained that when we deal with robotics, there are both design aspects and control aspects. Initially, classical control methods were common, but now many researchers, including himself, use machine learning and reinforcement learning to handle more complex tasks and improve the robot’s performance.

He said that while earlier (about 10 or 15 years ago) robots operated mostly on classical control without any machine learning, today the field is evolving. Controlling a quadruped involves understanding its dynamics, and since it must walk on uneven terrains and handle more complicated situations than a wheeled robot, applying newer methods like reinforcement learning becomes important.

Key Subjects and Topics for Robotics and Mechanical Design

According to Mr. Hercules, certain subjects are very important for anyone who wants to excel in robotics and mechanical design. Mathematics forms the foundation. He mentioned:

• Linear algebra

• Calculus

• Differential equations

• Complex numbers

• Laplace transforms

These mathematical tools help in understanding kinematics and dynamics of robotic systems. For controlling a robot, one must be able to write the differential equations that describe the system’s behavior. These equations tell you about the state of the robot (its position, orientation, and so on) at any given time, and by solving them, you can predict what the robot will do at the next time step. This predictive capability is essential for control.

Furthermore, when one moves into machine learning and reinforcement learning, probability and statistics become crucial. Thus, overall, mathematics, including these advanced topics, and a good understanding of probability and statistics, form the backbone of both traditional and modern robotics.

Design Tools and Mechanical Analysis

On the design side, Mr. Hercules explained that mechanical design is not just about aesthetics. It involves stress analysis, strain analysis, and ensuring that the parts can handle the loads they will experience. One must consider the strength of materials. Theoretical calculations can be done to figure out how much stress a certain geometry can withstand, and then advanced software can be used to verify and refine these calculations.

He mentioned using CAD (Computer-Aided Design) software like SOLIDWORKS and CATIA for building the 3D models of the robot. For analysis like stress and strain (Finite Element Analysis, FEA), tools like ANSYS or other software are often employed. This ensures that before a robot is built physically, one can confirm whether the design can bear specific loads or stresses.

Simulators and Virtual Testing

Once the design is ready, engineers and researchers often rely on simulators to test the robot’s performance before creating a physical prototype. Mr. Hercules said that after designing the robot, you can import its model into rigid body simulators. He mentioned using NVIDIA Isaac Sim, Mujoco, and VBot simulators.

These simulators allow you to give inputs to the robot model and see how it behaves in a virtual environment. This is crucial because it lets you test different control strategies, see how the robot might react under various conditions, and make improvements without risking expensive hardware or running unsafe tests in the real world.

Projects to Explore in Robotics

When asked about what kind of projects students could work on, Mr. Hercules pointed out several interesting areas:

1. Quadruped and Humanoid Robots:
   Quadrupeds and humanoids are hot topics. They can walk on uneven terrain, climb stairs, jump, and carry payloads where drones or wheeled robots cannot easily operate. The control part for such robots is very challenging, and researchers are now using machine learning and reinforcement learning to solve these problems.

2. Autonomous Wheeled Robots:
   Autonomous wheeled robots (like in warehouses) can navigate the environment, pick up products, and bring them to a certain place. This reduces manual labor. Companies like Amazon and Flipkart could use such robots for efficient inventory management.

3. Drones:
   Drones have a wide range of applications—photography, agriculture, and even warfare. They can do surveillance, mapping, and carry out tasks in areas that might be dangerous or difficult for humans.

4. Biped Robots and Robotic Hands:
   In robotics, developing biped robots that walk like humans or creating robotic hands that can grasp objects is another challenging area. Each part of a humanoid robot, including its hands, can have separate control and design problems.

5. Soft Robotics:
   Soft robotics does not rely on conventional motors for actuation. Instead, it uses alternative methods to move links. This is a growing field that focuses on compliant mechanisms and can be used in areas where delicate handling is needed.

6. Space Robotics:
   Space robotics involves designing robots that meet specific requirements to operate in space conditions.

Mr. Hercules is personally working on a project that involves “design improvement of a leg of a quadruped using reinforcement learning.” By using machine learning for control and improving the design aspects of the robot’s leg, he hopes to achieve better performance and adaptability.

Reinforcement Learning in Robotics

Mr. Hercules explained reinforcement learning (RL) by drawing a parallel to how a child learns to ride a bicycle. The child tries pedaling in different ways—if he falls, he learns not to do that again. Over time, he learns what actions help him stay balanced. This trial-and-error approach is similar to RL.

In reinforcement learning, the robot or algorithm tries different actions and observes the outcomes. It does not need labeled data like in supervised learning. Instead, it learns from the experiences it has with the environment. If the robot does something that leads to a good outcome, it gets a reward. If it leads to a bad outcome, it receives a penalty. Over many trials, the robot figures out which actions yield the best results.

He emphasized that we cannot physically do millions of trial-and-error experiments on a real robot because it would be too risky, time-consuming, and could damage the hardware. This is why we use a simulator. The simulator can run millions of iterations in just seconds. This allows the algorithm to quickly find out what works best. The robot “learns” in the simulator by experiencing multiple scenarios and refining its strategy. Once it learns a good policy or control method, we can transfer that knowledge to the real robot, making it more capable in the real world.

He also noted that reinforcement learning can be applied not just in robotics but in many fields—like chemical engineering, aerospace, or any area where you can define an environment and let an agent learn by trial and error.

Advice for Students Interested in Robotics and Reinforcement Learning

Mr. Hercules suggested that students should first understand the fundamentals of robotics. They need to know the physical behavior of robots, learn classical control methods, and grasp the underlying mathematics, including dynamics, kinematics, and how to write and solve differential equations.

After mastering these basics, students can then explore reinforcement learning and machine learning techniques. Reinforcement learning is like a tool that can be applied once you know how the robot behaves physically. Classical control works very well in stable, unchanging environments, such as an industry setting where a robot performs repetitive tasks perfectly with a well-defined controller.

However, when the environment keeps changing, classical control might not be sufficient. If you want a robot to operate in dynamic and unpredictable scenarios, reinforcement learning helps because it deals with probabilities and adaptations. Thus, knowing both conventional robotics and advanced reinforcement learning methods will give students a better career path and more significant opportunities.

He also mentioned that while design is important, it may become saturated if it does not incorporate these advanced methods. Companies and research labs value engineers and researchers who can handle both the traditional design aspects and advanced control methods like reinforcement learning.

Market Opportunities and Industrial Applications

Mr. Hercules gave examples of where robotics is headed:

• Warehousing and Inventory Management: Companies like Amazon and Flipkart can use autonomous robots to handle products, reducing the workload on humans.

• Defense and Army Applications: The army might need quadruped robots, drones, and even jet suits to operate in various conditions. Autonomous robots can do surveillance, carry loads, or help in specific missions.

• Drones in Agriculture, Photography, and Warfare: Drones are increasingly used in diverse fields, from spraying crops to capturing unique photography angles to assisting in warfare scenarios.

• Home Automation: Cleaning robots and other household robots can autonomously navigate homes, avoiding obstacles and performing tasks without continuous human guidance.

• Autonomous Vehicles: Companies like Ola, Tesla, and others are working on self-driving cars that rely on robotics, machine learning, and advanced navigation systems.

While hardware for many of these robots already exists, the challenge lies in controlling them intelligently—adapting to changing conditions, avoiding obstacles, and making smart decisions. Reinforcement learning and machine learning are key to making these robots smarter and more useful in real-world situations.

Advantages of Higher Studies at Top Institutes

Having completed his Master’s at IIT Bhubaneswar and now pursuing a Ph.D. at IISc Bangalore, Mr. Hercules said these experiences gave him deep mathematical and analytical skills, as well as programming abilities in tools like MATLAB or Python. He can model systems, understand physical phenomena, and apply these concepts to robotics and control problems.

He mentioned that earlier, during his B.Tech, there was always a concern about getting a job. But after gaining these advanced skills at top institutes, he is more confident. He learned how to break down complex problems, model them, and find control solutions. This confidence comes from understanding the fundamentals deeply and being comfortable with advanced methods.

He also noted that pursuing a Ph.D. allows him to collaborate with companies and other top universities, like Georgia Tech. For instance, if they have a biped hardware abroad, he might develop control algorithms from here, and they deploy it there. These collaborations, interactions, and exposure to real-life problems further enhance his skill set and understanding of the global robotics landscape.

Conclusion

Mr. Hercules’s journey and insights show that a strong foundation in mathematics, classical control, and robotics fundamentals is essential before moving on to advanced techniques like reinforcement learning. Students should:

• Understand the basic physics and mathematics behind robotic systems.

• Learn classical control methods thoroughly.

• Use CAD and simulation tools to design and virtually test robots.

• Explore reinforcement learning and machine learning to handle dynamic, unpredictable environments.

• Keep abreast of industry trends, as many sectors—from warehousing to defense, from agriculture to home automation—are increasingly relying on robotics and intelligent control solutions.

By integrating traditional robotics knowledge with modern machine learning and reinforcement learning approaches, students can position themselves well for future opportunities in this rapidly evolving field. The combination of strong theoretical foundations, practical simulation skills, and exposure to real-world applications will enable them to contribute meaningfully to robotics research and industry.