AI Powered Humanoid Bot

Project Overview

Project Name: Humanoid Bot – AI-Powered Human Simulation System
Duration: 8-12 weeks

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Description

This project focuses on the design and development of a humanoid robot that simulates human-like interaction, movements, and communication using artificial intelligence. Students will explore fields such as robotics, natural language processing, computer vision, and embedded systems to build a bot that can mimic real-world human behavior in various interactive scenarios.

Learning Objectives
By completing this project, students will
  • Understand the fundamentals of humanoid robotics and AI integration
  • Learn mechanical and electronic design principles in robotics
  • Explore natural language processing and speech synthesis
  • Develop face and object recognition systems using computer vision
  • Integrate sensor-based environment interaction systems
  • Apply reinforcement learning and gesture control
  • Understand human-robot interaction (HRI) models
  • Gain experience in real-time control and embedded programming

Project Scope and Features

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Human-like Communication Interface

  • Speech recognition and text-to-speech interaction
  • Chatbot-based conversational intelligence
  • Emotion detection and sentiment response
  • Multilingual communication capabilities

Facial and Gesture Recognition System

  • Real-time face tracking and detection
  • Expression analysis and response generation
  • Hand gesture recognition using cameras
  • Visual cue-based interaction
2
3

Motion and Movement Control

  • Robotic limb articulation (arms, head, torso movement)
  • Inverse kinematics for balance and natural posture
  • Walking simulation (if legs are integrated)
  • Obstacle avoidance and path planning

Sensor Integration and Environmental Awareness

  • Ultrasonic and IR sensors for proximity detection
  • Touch and pressure sensors for safety
  • Temperature and light sensors for adaptive behavior
  • Microphone arrays for directional hearing
4

Technical Framework Requirements

Core Technologies and Concepts

  • AI Agent Architectures: Reactive agents, deliberative agents, hybrid architectures
  • Natural Language Processing: Intent recognition, entity extraction, dialogue management
  • Machine Learning Techniques: Classification, clustering, regression, deep learning
  • Recommendation Systems: Collaborative filtering, content-based filtering, hybrid approaches
  • Multi-Agent Systems: Agent communication, coordination, negotiation protocols

System Architecture Components

  • Customer data integration and management platforms
  • AI agent orchestration and coordination frameworks
  • Real-time communication and interaction processing
  • Analytics and business intelligence dashboards
  • Workflow automation and business process engines
  • Security and privacy protection mechanisms

Phase-wise Implementation Strategy

Phase 1: CRM and Business Process Foundation (Week 1-2)

Customer Relationship Management Theory
CRM Fundamentals
  • Customer lifecycle stages and journey mapping
  • Sales pipeline management and opportunity tracking
  • Customer service processes and support workflows
  • Marketing automation and campaign management
  • Customer data management and data quality principles
  • Business process modeling and optimization
Customer Experience Strategy
  • Omnichannel customer engagement principles
  • Customer touchpoint identification and optimization
  • Service quality measurement and improvement
  • Customer satisfaction and Net Promoter Score(NPS) analysis
  • Voice of Customer(VoC) programs and feedback management
Business Domain Research
Industry-Specific CRM Applications
  • B2B vs B2C customer relationship differences
  • Sector - specific requirements(retail, healthcare, finance, manufacturing)
  • Enterprise vs SMB CRM needs and constraints
  • Global vs local market considerations
  • Regulatory compliance requirements by industry
Digital Transformation in Customer Management
  • Traditional CRM limitations and pain points
  • AI - driven transformation opportunities
  • Integration with existing business systems
  • Change management and user adoption strategies
  • ROI measurement and success metrics

Phase 2: Lane Detection and Vehicle Control (Week 3–4)

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Vision Pipeline for Lane Detection
  • ROI (Region of Interest) extraction
  • Grayscale and Gaussian blur preprocessing
  • Lane curve estimation using Hough lines
Control System Integration
  • Implement PID controller for steering
  • Adjust vehicle speed based on road curvature
  • Initial tests in simulation environment

Phase 3: Object Detection and Traffic Sign Recognition (Week 5–6)

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Object Detection
  • Implement YOLOv5/SSD model for obstacle detection
  • Annotate and use datasets for vehicle and pedestrian detection
  • Distance estimation from camera input
Traffic Sign Detection
  • CNN model training on German Traffic Sign Dataset (GTSRB)
  • Classify signs like Stop, Turn Left/Right, Speed Limit
  • Connect recognition to driving decisions

Phase 4: Path Planning and Navigation (Week 7–8)

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Trajectory Planning
  • Implement waypoint system with adjustable turns
  • Use A* or Dijkstra for obstacle-avoiding routing
  • Integrate path smoothing for sharp turns
Autonomous Navigation in Simulated Environment
  • Run trials in CARLA/Udacity Simulator
  • Adjust path in real time based on vision inputs

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Our Locations

Come and chat with us about your goals over a cup of coffee.

Hyderabad, Telangana

2nd Floor, Hitech City Rd, Above Domino's, opp. Cyber Towers, Jai Hind Enclave, Hyderabad, Telangana.

Bengaluru, Karnataka

3rd Floor, Site No 1&2 Saroj Square, Whitefield Main Road, Munnekollal Village Post, Marathahalli, Bengaluru, Karnataka.