India's first research-grade robotics curriculum built on top of AI foundations. Go beyond mechanical control — build autonomous agents that see, decide, and act using Computer Vision, Reinforcement Learning, and Edge AI. Designed exclusively for engineers who already understand code and want to work at the frontier.
Each track is a standalone research-grade course. Take one, stack them, or go all-in with the full bundle. Every track earns you a verifiable skill module on your LinkedIn profile.
Real-time perception for autonomous systems. Object detection, depth estimation, semantic segmentation, and live inference on edge hardware — the eyes of every modern robot.
Train agents to navigate environments, manipulate objects, and make real-world decisions. From Q-Learning to PPO — the intelligence layer behind autonomous robotics at Tesla, Boston Dynamics, and warehouse automation.
Deploy trained models on microcontrollers and single-board computers. TensorFlow Lite, ONNX Runtime, model quantisation and pruning — the bridge between "I trained a model" and "it runs in the real world."
Build an end-to-end autonomous robot from scratch — perception pipeline, decision-making engine, actuation layer, and deployment on real hardware. Your capstone project becomes the centrepiece of your robotics portfolio.
Every module is built around a hands-on lab exercise. No theoretical fluff — every concept is validated by running code on actual hardware or simulation.
Every track is aligned with active industry research problems. Students engage with current papers, replicate experiments, and contribute to open-source robotics projects.
SLAM, localisation, path planning, and obstacle avoidance in dynamic, unstructured environments. Applicable to delivery drones, AGVs, and self-driving vehicles.
Grasp planning, dexterous manipulation, and task-and-motion planning for industrial and surgical robotics. High-demand in pharma automation and smart manufacturing.
Gesture recognition, intent prediction, and collaborative task execution. Foundational for service robots, exoskeletons, and assistive technology.
Swarm intelligence, fleet management, and distributed task allocation. Core to Amazon warehousing, drone swarms, and industrial IoT.
Domain randomisation, physics engine calibration, and transfer learning strategies to close the gap between simulation training and real-world deployment.
Precision actuation for surgical assistance, prosthetic limb control using EMG signals, and rehabilitation exoskeletons. India's fastest-growing medtech robotics segment.
Every student completes 3 portfolio projects. These aren't toy demos — they are production-grade systems that solve real engineering problems.
Build a mobile ground robot that uses a camera + YOLOv8 for obstacle detection, ROS2 for navigation, and a PPO-trained policy for dynamic path replanning. Deployed on actual hardware in a timed obstacle course.
Train a vibration anomaly detection model using sensor data from industrial equipment, quantise it to TFLite INT8, and deploy on a Raspberry Pi 5 with sub-20ms inference latency. Tested against real failure datasets.
Train a RL agent in Isaac Sim to navigate a complex 3D environment with moving obstacles, then transfer the policy to a physical differential-drive robot using domain randomisation techniques.
Build a robot that constructs a 2D occupancy map of an unknown indoor environment in real-time using LiDAR + camera fusion, localises itself within the map, and autonomously reaches designated waypoints.
Everything taught is what robotics teams at ISRO, Ola Electric, and global autonomous vehicle companies actually use.
Robotics + AI is the most under-supplied skill set in Indian tech right now. Companies are hiring aggressively — and salaries reflect the scarcity.
A LinkedIn-verified credential recognising end-to-end robotics AI competency — from perception and learning to edge deployment and autonomous systems. The rarest badge in Newton JEE's portfolio.
Unlike attendance certificates, our Robotics Research Badge is competency-verified. You earn it by building and demonstrating a working autonomous system — not by completing quizzes.