Challenge Overview

The AI & Robotics Hackathon at RAAIF NextGen Expo 2026 brings together the brightest minds to address critical automation tasks. This challenge is designed to push the boundaries of conventional mechanics by injecting advanced artificial intelligence into physical robot systems.

Challenge Streams

Teams can choose to participate in one of three specialized engineering tracks:

1. AI — Intelligent Real-World Assistance

Problem Statement:
Develop an AI-powered software or hardware solution that can perceive and interpret real-world data to assist human operators in making better, faster, or more informed decisions.

Solutions should utilize Computer Vision, Generative AI, Machine Learning, Agentic AI, Speech AI, or multimodal LLMs to address a practical bottleneck in domains such as manufacturing, logistics, retail, accessibility, public services, or healthcare.

Expected: PROBLEM → AI UNDERSTANDING → INTELLIGENT ACTION → REAL-WORLD IMPACT

2. Robotics — Autonomous Problem Solver

Problem Statement:
Develop a physical robotic system capable of perceiving its environment, determining path trajectories, and executing a physical task with minimal human intervention.

The robot can target applications such as industrial manipulation, warehouse inventory tracking, hazardous inspections, smart agriculture, sorting systems, disaster response, or healthcare logistics.

Expected: SENSE → THINK → ACT → SOLVE

3. AI + Robotics — Intelligent Autonomous System

Problem Statement:
Create an intelligent integrated robotic system that combines machine learning/AI models directly with physical mechanics to solve a problem that is difficult to address using conventional programming alone.

The system must demonstrate a meaningful loop: the robot perceives surroundings, interprets data through local/cloud AI pipelines, triggers physical task decisions, and refines mechanical movements dynamically.

Expected: PERCEIVE → UNDERSTAND → DECIDE → ACT

BUILD INTELLIGENCE THAT CAN ACT.

Your AI should think. Your robot should act. Your solution should solve.

Evaluation Criteria

Design Approach: Clean kinematics, robust chassis, and logical system layout.
AI Integration: Meaningful use of machine learning, classification, or LLM reasoning.
Prototype Maturity: Operational reliability of sensors, actuators, and code pipelines.
Real-World Applicability: Scalability of the prototype in industrial or social contexts.

Expected Outputs

Participants must present the following during the evaluation round:

A working physical prototype (or simulation with physical interface).
Problem definition document and system architecture diagram.
AI/Robotics framework diagram showing the data loop.
Live demonstration showing autonomous task completion.

AI & Robotics Hackathon