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Free Download Udemy - HMOD102 - Advanced Humanoid Robotics in Action
Published: 4/2025
Created by: Dr Samuel Xiangming Li
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 10 Lectures ( 1h 35m ) | Size: 847 MB
Integrating Generative AI ᑕᕼᗩTGᑭT into your Humanoid Robots!
What you'll learn
Introduction to Generative AI: Explore AI fundamentals and its role in humanoid robots.
ᑕᕼᗩTGᑭT for Robotics: Enhance robot communication and decision-making with AI.
Humanoid Robot Programming: Hands-on Python programming with Yanshee robots.
AI-Driven Behaviors: Learn real-time adaptive AI for dynamic environments.
Emotion Detection: Implement AI to recognize and respond to human emotions.
Capstone Project: Build an AI-powered humanoid for real-world applications.
Requirements
Prerequisite: Completion of HMOD101: Humanoid Robotics in Action
Description
This course equips students with cutting-edge skills to develop humanoid robots using Generative AI, specifically ᑕᕼᗩTGᑭT, to enhance real-world applications in industries such as healthcare, customer service, and industrial automation. Students will build AI-powered humanoids capable of dynamic interactions, emotion detection, and autonomous task execution. Designed with a practical, hands-on approach, this course appeals to Udemy learners seeking project-based learning and in-demand skills for careers in AI and robotics.This advanced course takes humanoid robotics to the next frontier by integrating Generative AI, specifically OpenAI's ᑕᕼᗩTGᑭT, with Yanshee humanoid robots. Students will explore the revolutionary potential of Generative AI in creating more intelligent, responsive, and interactive humanoids capable of performing complex tasks, holding human-like conversations, and autonomously adapting to dynamic environments. The course focuses on innovative applications of ᑕᕼᗩTGᑭT, opening new possibilities in robotics development, and positioning learners at the cutting edge of the humanoid robotics revolution.By the end of this course, students will be able to:Integrate ᑕᕼᗩTGᑭT and other Generative AI models with humanoid robots to enhance communication and decision-making capabilities.Program humanoid robots for real-time, natural human-robot interaction using Generative AI.Design autonomous, AI-powered humanoids capable of complex problem-solving.Build full-stack humanoid systems with AI-driven capabilities for specific industries (healthcare, education, entertainment).Develop humanoids capable of adapting their behaviors using real-time feedback from Generative AI models.Course Syllabus can be found and downloadable at Section #1: Downloadable Resource Weekly Module Schedule: Module/Week 1: Introduction to Generative AI in Robotics· This Module Learning Objectives:By the end of this course, students will be able to
Understand the role of Generative AI in humanoid robotics.o Explore ᑕᕼᗩTGᑭT's potential to improve communication and interaction between robots and humans.o Set up and integrate ᑕᕼᗩTGᑭT with Yanshee humanoid robots.o Examine recent breakthroughs in humanoid robots from companies like Boston Dynamics and Unitree.· Topics Overview of Generative AI and its capabilitieso Role of ᑕᕼᗩTGᑭT in AI-human interactiono Recent breakthroughs in humanoid robotics (Figure 2, Optimus, Unitree, Boston Dynamics)o Introduction to Yanshee humanoid robot and its programming environmento Setting up the environment for ᑕᕼᗩTGᑭT integration with Yanshee· Tasks: Set up OpenAI GPT-4 API for interaction with humanoid robotsModule/Week 2: ᑕᕼᗩTGᑭT Integration with Yanshee· This Module Learning Objectives:By the end of this course, students will be able to Integrate GPT-4 into Yanshee's communication systems.o Program humanoid robots to use Generative AI for dynamic conversational interactions.o Implement APIs for real-time cloud-based AI communication.o Develop use cases for ᑕᕼᗩTGᑭT-powered robots in industries such as healthcare and customer service.· Topics Connecting Yanshee's communication system to ChatGPTo APIs and frameworks for connecting robots to cloud-based AI modelso Programming humanoid robots to generate natural language responseso Use cases: ᑕᕼᗩTGᑭT-powered robots in customer service, healthcareo Building basic conversation flows between humans and humanoids· Tasks: Implement a basic ᑕᕼᗩTGᑭT-powered conversational interface on YansheeModule/Week 3: Enhancing Human-Robot Interaction Using ᑕᕼᗩTGᑭT· This Module Learning Objectives:By the end of this course, students will be able to Design conversational flows for humanoid robots using Generative AI.o Implement emotion detection and personalized interaction capabilities in humanoid robots.o Create intelligent responses based on ambiguous and incomplete inputs.o Address ethical considerations for AI-human interactions (e.g., disclosing robot identity).· Topics Designing natural conversations with Generative AIo Emotion detection and personalized interaction with robotso Handling ambiguous and incomplete inputs with AIo Optimizing conversation flows for different use caseso Ethical considerations: When should humanoid robots disclose they are AI?Tasks: Create an intelligent conversational agent on Yanshee for specific user scenarios (e.g., healthcare assistant)Module/Week 4: Generative AI for Autonomous Decision-Making in Humanoids· This Module Learning Objectives:By the end of this course, students will be able to Design conversational flows for humanoid robots using Generative AI.o Implement emotion detection and personalized interaction capabilities in humanoid robots.o Create intelligent responses based on ambiguous and incomplete inputs.o Address ethical considerations for AI-human interactions (e.g., disclosing robot identity).· Topics Using ᑕᕼᗩTGᑭT for real-time problem-solving and decision-makingo Training humanoids to understand complex scenarios and offer solutionso Integrating external data sources (e.g., databases, APIs) for smarter responseso Cognitive AI in humanoid robotics: Learning from interactionso Designing humanoids capable of multi-tasking· Tasks: Lab on integrating ᑕᕼᗩTGᑭT for task-oriented decision-makingModule/Week 5: Adaptive Learning and Feedback Systems in Humanoids· This Module Learning Objectives:By the end of this course, students will be able to Implement adaptive learning using reinforcement learning and generative feedback loops.o Teach humanoid robots to learn from real-time human interactions.o Develop systems that allow robots to adapt to changing environments.o Analyze case studies of adaptive robots in fields like education and therapy.· Topics Reinforcement learning vs. generative feedback loopso Teaching humanoid robots to learn from user interactionso Adaptive behavior: Responding to changing environmentso Case study: Adaptive humanoids in education and therapyo Evaluating performance and improving conversational accuracy over time· Tasks: Develop an adaptive learning system for Yanshee using ᑕᕼᗩTGᑭT and real-time feedbackModule/Week 6: Full-Stack Development: Building Intelligent Humanoids· This Module Learning Objectives:By the end of this course, students will be able to Integrate AI with hardware components (sensors, motors, cameras) for humanoid robots.o Build full-stack applications that utilize ᑕᕼᗩTGᑭT for humanoid interactions.o Use cloud platforms for AI model deployment and processing.o Test and refine humanoid robot performance in real-world scenarios.· Topics Hardware integration: Sensors, motors, and cameras with AIo Developing full-stack robotic applications with ChatGPTo Use of cloud platforms for AI model hosting (AWS, Google Cloud)o Advanced humanoid tasks: real-time navigation and manipulationo Testing and refining robot performance in real-world settings· Tasks: Develop a fully functioning humanoid system using ᑕᕼᗩTGᑭT for dynamic interactionsModule/Week 7: Midterm Project Presentation· This Module Learning Objectives:By the end of this course, students will be able to Present a functional humanoid robot integrated with ᑕᕼᗩTGᑭT.o Demonstrate the robot's conversational and interactive capabilities.o Evaluate peer feedback and refine robot designs.o Apply learned skills to improve robot functionality and task performance.· Tasks: Midterm project presentations; peer and instructor feedbackModule/Week 8: Emotional Intelligence in Humanoids· This Module Learning Objectives:By the end of this course, students will be able to Equip humanoid robots with emotion detection algorithmso Create AI-driven emotional responses in robots for social interactions.o Explore applications of emotionally intelligent robots in healthcare and customer service.o Evaluate ethical considerations when designing emotionally responsive robots.· Topics Emotion detection algorithms for Generative AIo Emotional response generation through ChatGPTo Practical applications: Social robots for therapy, customer service, entertainmento Ethical considerations of emotionally responsive robotso Designing emotionally intelligent humanoid systems· Tasks: Program Yanshee to detect and respond to emotional cues using ChatGPTModule/Week 9: Collaborative Robotics (Cobot) with ᑕᕼᗩTGᑭT· This Module Learning Objectives:By the end of this course, students will be able to Design collaborative humanoids using ᑕᕼᗩTGᑭT as a communication mediator.o Develop multi-agent systems for robots to collaborate with humans and other robots.o Program robots to coordinate tasks in real-time for manufacturing and logistics.o Analyze case studies on AI-powered cobots in industry.· Topics Generative AI in multi-agent systems (robot teams)o Designing collaboration protocols for humanoid robotso ᑕᕼᗩTGᑭT as a mediator in human-robot collaborationo Case study: AI-driven cobots in manufacturing and logisticso Coordinating complex tasks between robots and humans· Tasks: Create a collaborative task between Yanshee and another robot using ChatGPTModule/Week 10: Advanced Mobility and Task Execution Using AI & Capstone & Granulations!· This Module Learning Objectives:By the end of this course, students will be able to Implement advanced mobility features using AI-driven control algorithms.o Enable humanoids to perform complex task scheduling and execution.o Develop motion planning for humanoid robots to navigate dynamic environments.o Explore real-world case studies on robots operating in challenging settings.· Topics Advanced motion planning and obstacle avoidanceo Generative AI-driven task scheduling and executiono Gait control algorithms for humanoid locomotiono Using ᑕᕼᗩTGᑭT for dynamic task prioritizationo Case study: Humanoid robots in complex, changing environments· Tasks: Implement advanced mobility control and AI-driven task execution on Yanshee7 Lab Demos using Python on Yanshee Humanoid Platform (*Python source code * is available @Udemy Section/Class!): 1. Control Yanshee Humanoid Movement: forward, left turn, right turn, backward, dance, etc.;2. Integrate ᑕᕼᗩTGᑭT APIs to enable Humanoid to have intelligent conversation (ChatBot) 1/2;3. Integrate ᑕᕼᗩTGᑭT APIs to enable Humanoid to have intelligent conversation (ChatBot) 2/2;4. Detect the human facial Emotion such as Smile, Sad, Happy, Bored, etc 1/2;5. Detect the human facial Emotion such as Smile, Sad, Happy, Bored, etc 2/2;6. Recognize the human Gestures like wave hand, welcome, bye-bye, etc. 1/2;7. Recognize the human Gestures like wave hand, welcome, bye-bye, etc. 2/212 Lecture PPTs can be downloadable from each section at Downloadable Resource for your reference.Who this course is for
For students who are interested in Humanoid Robotics
Building advanced humanoid robots for intelligent chat with ᑕᕼᗩTGᑭT, Emotion Detection and Gesture Control
Various Humanoid applications in Healthcare, Education, Home Companionship, Manufacturing Cobot, etc.
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