Educational Robotics with Reachy Mini

# Elementary Level (Ages 8-12) - Visual Programming Introduction # Using simplified block-based programming concepts class ElementaryRoboticsLesson: def __init__(self, reachy): self.reachy = reachy self.lesson_concepts = [ "basic_movements", "sequences", "loops", "conditionals" ] def teach_basic_movements(self): """Lesson 1: Understanding robot movement""" print("Let's make Reachy look around!") # Simple movement sequence movements = [ ("Look Up", (0, 0.3, 0)), ("Look Down", (0, -0.3, 0)), ("Look Left", (-0.3, 0, 0)), ("Look Right", (0.3, 0, 0)), ("Look Center", (0, 0, 0)) ] for description, position in movements: print(f"Step: {description}") self.reachy.head.look_at(position[0], position[1], position[2], duration=2.0) time.sleep(1) def teach_sequences(self): """Lesson 2: Creating movement sequences""" print("Creating a greeting sequence!") # Greeting sequence sequence = [ "center_position", "look_at_person", "nod_hello", "return_center" ] self.execute_sequence(sequence) def teach_loops(self): """Lesson 3: Repeating actions with loops""" print("Learning about loops - making Reachy nod 5 times!") for i in range(5): print(f"Nod number {i+1}") self.reachy.head.look_at(0, 0.2, 0, duration=0.5) self.reachy.head.look_at(0, -0.2, 0, duration=0.5) self.reachy.head.look_at(0, 0, 0, duration=0.5) def simple_interaction_game(self): """Interactive game for elementary students""" print("Simon Says Robot Edition!") commands = [ ("Simon says look up!", (0, 0.3, 0)), ("Simon says look down!", (0, -0.3, 0)), ("Look left!", (-0.3, 0, 0)), # Trick command ("Simon says shake your head!", "shake_head") ] for command, action in commands: print(f"Command: {command}") input("Press Enter when students are ready...") if "Simon says" in command: if isinstance(action, tuple): self.reachy.head.look_at(action[0], action[1], action[2], duration=1.0) elif action == "shake_head": self.shake_head() else: print("Reachy doesn't move - Simon didn't say!") time.sleep(2) # Middle School Level (Ages 12-16) - Programming Fundamentals class MiddleSchoolRoboticsLab: def __init__(self, reachy): self.reachy = reachy self.student_projects = {} self.learning_objectives = [ "variables", "functions", "sensors", "decision_making", "basic_ai" ] def project_1_personality_robot(self): """Project 1: Programming robot personality""" print("Programming Reachy's Personality!") # Students can customize these parameters personality_config = { "energy_level": "high", # high, medium, low "friendliness": "very_friendly", # shy, friendly, very_friendly "curiosity": "curious", # indifferent, interested, curious "responsiveness": "quick" # slow, normal, quick } # Demonstrate personality through movements self.express_personality(personality_config) def express_personality(self, config): """Express robot personality through movement""" if config["energy_level"] == "high": # Quick, energetic movements self.reachy.head.look_at(0.2, 0.1, 0, duration=0.5) self.reachy.head.look_at(-0.2, 0.1, 0, duration=0.5) self.reachy.head.look_at(0, 0, 0, duration=0.3) if config["friendliness"] == "very_friendly": # Welcoming gestures for _ in range(3): self.reachy.head.look_at(0, 0.2, 0, duration=0.4) self.reachy.head.look_at(0, 0, 0, duration=0.4) def project_2_sensor_response(self): """Project 2: Responding to environmental inputs""" print("Teaching Reachy to respond to the environment!") # Simulated sensor reading project sensor_scenarios = [ {"light_level": "bright", "noise_level": "quiet"}, {"light_level": "dim", "noise_level": "loud"}, {"light_level": "normal", "noise_level": "normal"} ] for scenario in sensor_scenarios: print(f"Scenario: Light={scenario['light_level']}, Noise={scenario['noise_level']}") self.respond_to_environment(scenario) time.sleep(2) def respond_to_environment(self, sensors): """Demonstrate conditional responses""" if sensors["light_level"] == "bright": # Squint gesture self.reachy.head.look_at(0, -0.1, 0, duration=1.0) elif sensors["light_level"] == "dim": # Look around curiously self.reachy.head.look_at(0.3, 0, 0, duration=0.8) self.reachy.head.look_at(-0.3, 0, 0, duration=0.8) if sensors["noise_level"] == "loud": # Alert posture self.reachy.head.look_at(0, 0, 0.2, duration=0.5) # High School/College Level (Ages 16+) - Advanced Programming class AdvancedRoboticsWorkshop: def __init__(self, reachy): self.reachy = reachy self.advanced_concepts = [ "computer_vision", "machine_learning", "path_planning", "human_interaction", "system_integration" ] def computer_vision_project(self): """Advanced Project: Computer Vision Integration""" import cv2 import numpy as np # Face detection project face_cascade = cv2.CascadeClassifier( cv2.data.haarcascades + 'haarcascade_frontalface_default.xml' ) cap = cv2.VideoCapture(0) while True: ret, frame = cap.read() if not ret: break gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, 1.3, 5) for (x, y, w, h) in faces: # Calculate face center face_center_x = x + w // 2 face_center_y = y + h // 2 # Convert to robot coordinates frame_center_x = frame.shape[1] // 2 frame_center_y = frame.shape[0] // 2 # Calculate robot movement look_x = (face_center_x - frame_center_x) / frame_center_x * 0.3 look_y = -(face_center_y - frame_center_y) / frame_center_y * 0.3 # Move robot to look at face self.reachy.head.look_at(look_x, look_y, 0, duration=0.5) # Draw rectangle around face for debugging cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2) cv2.imshow('Face Tracking', frame) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows() def machine_learning_behavior(self): """Advanced Project: Learning Robot Behaviors""" # Simple reinforcement learning example import random # State: [person_present, interaction_positive, time_of_day] # Actions: [greet, wave, nod, ignore] q_table = {} actions = ["greet", "wave", "nod", "ignore"] learning_rate = 0.1 discount_factor = 0.9 def get_state(): # Simplified state representation person = random.choice([True, False]) positive = random.choice([True, False]) time_period = random.choice(["morning", "afternoon", "evening"]) return (person, positive, time_period) def choose_action(state, exploration_rate=0.1): if random.random() < exploration_rate: return random.choice(actions) else: # Choose best known action if state not in q_table: q_table[state] = {action: 0 for action in actions} return max(q_table[state], key=q_table[state].get) def execute_robot_action(action): """Execute chosen action with robot""" if action == "greet": self.reachy.head.look_at(0, 0.2, 0, duration=1.0) self.reachy.head.look_at(0, 0, 0, duration=1.0) elif action == "wave": for _ in range(2): self.reachy.head.look_at(0.2, 0, 0, duration=0.5) self.reachy.head.look_at(-0.2, 0, 0, duration=0.5) elif action == "nod": self.reachy.head.look_at(0, 0.1, 0, duration=0.5) self.reachy.head.look_at(0, -0.1, 0, duration=0.5) self.reachy.head.look_at(0, 0, 0, duration=0.5) # ignore = no movement def calculate_reward(action, state): """Calculate reward for action-state pair""" person_present, interaction_positive, time_period = state if not person_present and action != "ignore": return -1 # Penalize unnecessary actions elif person_present and action == "ignore": return -2 # Penalize ignoring people elif person_present and interaction_positive and action in ["greet", "wave"]: return 5 # Reward friendly actions else: return 0 # Neutral reward # Training loop demonstration for episode in range(10): state = get_state() action = choose_action(state) print(f"Episode {episode + 1}: State={state}, Action={action}") execute_robot_action(action) reward = calculate_reward(action, state) # Update Q-table (simplified) if state not in q_table: q_table[state] = {action: 0 for action in actions} q_table[state][action] += learning_rate * reward time.sleep(2)

class STEMCurriculumIntegration: def __init__(self): self.standards_mapping = { "NGSS": { "K-2-ETS1-1": "Ask questions, make observations, and gather information", "3-5-ETS1-2": "Generate and compare multiple possible solutions", "MS-ETS1-3": "Analyze data from tests to determine design features", "HS-ETS1-4": "Use computer simulation to model impacts of proposed solutions" }, "CSTA": { "1A-AP-10": "Develop programs with sequences and simple loops", "1B-AP-15": "Create programs incorporating learned concepts", "2-AP-11": "Create clearly named variables that represent different data types", "3A-AP-16": "Design programs using control structures and recursion" }, "Mathematics": { "Geometry": "Coordinate systems and spatial reasoning", "Algebra": "Variables, functions, and algorithmic thinking", "Statistics": "Data collection, analysis, and interpretation" } } def design_standards_aligned_lesson(self, grade_level, standard_code): """Create lesson plans aligned with educational standards""" if standard_code == "NGSS K-2-ETS1-1": return { "title": "Robot Observation and Questions", "objective": "Students ask questions about robot behavior and make observations", "activities": [ "Watch Reachy Mini perform movements", "Generate questions about how robots work", "Record observations of robot responses", "Compare robot movements to human movements" ], "assessment": "Students create question lists and observation journals" } elif standard_code == "CSTA 1A-AP-10": return { "title": "Programming Robot Sequences", "objective": "Create programs with sequences and simple loops", "activities": [ "Program basic movement sequences", "Create greeting routines using loops", "Debug simple programming errors", "Test and refine robot programs" ], "assessment": "Students create working robot programs demonstrating sequences and loops" } def create_interdisciplinary_project(self, subjects, duration_weeks): """Design cross-curricular projects using robotics""" projects = { ("Math", "Science"): { "title": "Robot Navigation and Physics", "description": "Students program robot movements while calculating angles, distances, and applying physics concepts", "math_concepts": ["Coordinate geometry", "Trigonometry", "Measurement"], "science_concepts": ["Motion", "Forces", "Simple machines"], "duration": f"{duration_weeks} weeks" }, ("English", "Computer Science"): { "title": "Interactive Storytelling Robot", "description": "Program robot to act out stories with voice interaction and movement", "english_concepts": ["Narrative structure", "Character development", "Public speaking"], "cs_concepts": ["Programming logic", "User interaction", "Conditional statements"], "duration": f"{duration_weeks} weeks" }, ("Art", "Technology"): { "title": "Expressive Robot Performance", "description": "Create artistic performances using robot movements and programming", "art_concepts": ["Movement and dance", "Expression", "Performance art"], "tech_concepts": ["Programming", "Timing", "Coordination"], "duration": f"{duration_weeks} weeks" } } return projects.get(subjects, "Project combination not available") class AssessmentStrategies: def __init__(self): self.assessment_types = [ "formative", "summative", "peer_evaluation", "self_reflection", "portfolio", "performance" ] def create_rubric(self, project_type, skill_level): """Create assessment rubrics for robotics projects""" rubrics = { "programming_project": { "criteria": { "Code Functionality": { "Excellent": "Program runs flawlessly and achieves all objectives", "Good": "Program runs with minor issues, meets most objectives", "Developing": "Program partially works, meets some objectives", "Beginning": "Program has major issues, few objectives met" }, "Problem Solving": { "Excellent": "Demonstrates creative solutions and debugging skills", "Good": "Shows good problem-solving approach with some creativity", "Developing": "Basic problem-solving with teacher guidance", "Beginning": "Limited problem-solving, requires significant help" }, "Collaboration": { "Excellent": "Excellent teamwork, shares ideas, helps others", "Good": "Good collaboration, contributes to team efforts", "Developing": "Some collaboration, occasionally shares ideas", "Beginning": "Limited collaboration, works mostly independently" }, "Documentation": { "Excellent": "Clear, detailed documentation with explanations", "Good": "Good documentation with most details included", "Developing": "Basic documentation, some details missing", "Beginning": "Minimal documentation, difficult to understand" } } } } return rubrics.get(project_type, "Rubric not found") def design_portfolio_system(self): """Create student portfolio system for robotics learning""" portfolio_structure = { "Learning Journey": [ "Initial knowledge assessment", "Weekly reflection journals", "Skill progression tracking", "Challenge documentation" ], "Project Artifacts": [ "Code samples with explanations", "Video demonstrations", "Problem-solving documentation", "Peer feedback forms" ], "Growth Evidence": [ "Before/after skill comparisons", "Mistake analysis and learning", "Goal setting and achievement", "Future learning plans" ] } return portfolio_structure

class ClassroomSetupGuide: def __init__(self): self.setup_recommendations = { "physical_space": { "minimum_area": "6x6 feet per robot station", "table_height": "adjustable for different ages", "power_requirements": "outlets every 6 feet", "lighting": "natural light preferred, LED backup", "storage": "secure cabinet for robot storage" }, "technology_infrastructure": { "internet": "high-speed WiFi for all students", "computers": "1:1 or 2:1 student-to-device ratio", "display": "large screen for demonstrations", "charging": "central charging station for devices" }, "safety_equipment": { "first_aid": "basic first aid kit accessible", "emergency_stops": "clearly marked emergency procedures", "protective_gear": "safety glasses if needed", "workspace_marking": "clear boundaries for robot operations" } } def create_rotation_schedule(self, num_students, num_robots, class_duration): """Create efficient rotation schedules for limited robots""" students_per_robot = max(2, num_students // num_robots) rotation_time = class_duration // 3 # Allow 3 rotations per class schedule = { "setup": { "group_size": students_per_robot, "rotation_duration": f"{rotation_time} minutes", "activities_per_rotation": 3 }, "rotation_activities": { "Station 1": "Hands-on robot programming", "Station 2": "Planning and design work", "Station 3": "Documentation and reflection", "Station 4": "Peer review and testing" if num_students > num_robots * 6 else None }, "transition_time": "5 minutes between rotations" } return schedule def manage_collaborative_learning(self): """Strategies for effective group work in robotics""" collaboration_strategies = { "role_assignments": { "Lead Programmer": "Writes main code structure", "Test Engineer": "Tests and debugs programs", "Documentation Specialist": "Records process and learnings", "Integration Manager": "Connects different code components" }, "communication_protocols": { "daily_standup": "Brief check-in on progress and challenges", "code_reviews": "Regular peer review of programming work", "problem_solving": "Structured approach to debugging", "reflection_sessions": "End-of-class learning reflection" }, "conflict_resolution": { "disagreement_protocol": "Structured discussion format", "decision_making": "Consensus building techniques", "teacher_intervention": "Clear escalation procedures" } } return collaboration_strategies class ProgressTracking: def __init__(self): self.tracking_metrics = [ "programming_skills", "problem_solving", "collaboration", "technical_understanding", "creative_thinking" ] def create_skill_progression_map(self): """Map skill development progression in robotics education""" skill_levels = { "Beginner": { "programming": "Basic movement commands, simple sequences", "problem_solving": "Identifies problems with guidance", "collaboration": "Works in group with support", "technical_understanding": "Basic robot components knowledge", "creativity": "Follows provided examples" }, "Developing": { "programming": "Uses loops, conditionals, basic functions", "problem_solving": "Breaks down problems into steps", "collaboration": "Contributes ideas to group work", "technical_understanding": "Understands sensors and outputs", "creativity": "Modifies examples with personal touches" }, "Proficient": { "programming": "Complex logic, multiple functions, debugging", "problem_solving": "Systematic approach to challenges", "collaboration": "Facilitates group problem-solving", "technical_understanding": "Integrates multiple systems", "creativity": "Develops original solutions" }, "Advanced": { "programming": "Advanced concepts, optimization, documentation", "problem_solving": "Innovative approaches, helps others", "collaboration": "Mentors other students", "technical_understanding": "Explains complex concepts to others", "creativity": "Creates novel applications and projects" } } return skill_levels def generate_progress_report(self, student_data): """Generate comprehensive progress reports""" report_template = { "student_info": student_data, "current_skills": "Assessment of current skill levels", "growth_areas": "Specific areas showing improvement", "challenges": "Areas needing additional support", "next_goals": "Recommended next learning objectives", "parent_engagement": "Suggestions for home reinforcement" } return report_template