Lesson 5.1: Capstone Project - Autonomous Humanoid Task Definition
Overview
This lesson defines the scope and requirements for the capstone project: an autonomous humanoid robot that receives a voice command, plans a path, navigates obstacles, identifies an object using computer vision, and manipulates it. This project integrates all concepts learned in the previous modules.
Learning Objectives
By the end of this lesson, you should be able to:
- Define the complete scope and requirements for the autonomous humanoid capstone project
- Break down the project into manageable sub-tasks
- Identify dependencies between different components
- Establish success criteria and evaluation metrics
- Plan the integration of all previous modules' concepts
Capstone Project Overview
Project Goal
Create a simulated humanoid robot that can:
- Receive and understand a voice command ("Pick up the red cube")
- Plan a safe path to the target object
- Navigate around obstacles in the environment
- Identify the target object using computer vision
- Manipulate the object (pick it up and move it)
System Architecture
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Detailed Task Requirements
1. Voice Command Processing
Components: OpenAI Whisper, ROS 2 integration Requirements:
- Receive audio input from simulated microphone
- Convert speech to text using Whisper
- Publish transcribed text to ROS 2 topic
/voice_command - Handle ambient noise and voice activity detection
# config/capstone_voice_config.yaml
voice_processor:
ros__parameters:
# Whisper model configuration
model_name: "base.en"
sampling_rate: 16000
audio_chunk_duration: 1.0 # seconds per chunk
# Processing parameters
vad_threshold: 0.3 # Voice activity detection threshold
silence_duration_threshold: 2.0 # seconds of silence to trigger processing
min_voice_duration: 0.5 # minimum voice duration to process
# ROS 2 topics
audio_input_topic: "/audio/microphone"
voice_command_topic: "/capstone/voice_command"
transcription_confidence_threshold: 0.7
# Performance
enable_profiling: true
processing_frequency: 10.0 # Hz
2. Cognitive Planning with LLMs
Components: LLM integration, task decomposition, action sequencing Requirements:
- Process voice commands using LLM
- Decompose high-level commands into specific robot actions
- Generate action sequence based on environmental context
- Publish action plan to execution system
# Example: LLM cognitive planner for capstone
import rclpy
from rclpy.node import Node
from std_msgs.msg import String
from geometry_msgs.msg import PoseStamped
from sensor_msgs.msg import Image
from typing import Dict, List
class CapstoneCognitivePlanner(Node):
def __init__(self):
super().__init__('capstone_cognitive_planner')
# Subscriptions
self.voice_cmd_sub = self.create_subscription(
String,
'/capstone/voice_command',
self.voice_command_callback,
10
)
self.vision_result_sub = self.create_subscription(
String,
'/capstone/vision_result',
self.vision_result_callback,
10
)
# Publishers
self.action_plan_pub = self.create_publisher(
String,
'/capstone/action_plan',
10
)
self.nav_goal_pub = self.create_publisher(
PoseStamped,
'/capstone/navigation_goal',
10
)
# Internal state
self.current_vision_result = None
self.pending_command = None
self.get_logger().info('Capstone Cognitive Planner initialized')
def voice_command_callback(self, msg):
"""Process voice command and generate action plan"""
command = msg.data.lower()
self.get_logger().info(f'Received voice command: {command}')
# Store command for processing with vision data
self.pending_command = command
# If we have vision data, process immediately
if self.current_vision_result:
self.process_command_with_vision(command, self.current_vision_result)
def vision_result_callback(self, msg):
"""Process vision analysis results"""
self.current_vision_result = msg.data
# If we have a pending command, process it now
if self.pending_command:
self.process_command_with_vision(self.pending_command, self.current_vision_result)
self.pending_command = None
def process_command_with_vision(self, command, vision_result):
"""Process command with vision context using LLM"""
# Construct LLM prompt with command and vision context
prompt = f"""
You are a cognitive planner for an autonomous humanoid robot. Based on the following voice command and visual scene analysis, generate a detailed action sequence.
Voice Command: "{command}"
Visual Scene Analysis: "{vision_result}"
Generate a JSON response with the following structure:
{{
"action_sequence": [
{{
"action": "navigate_to_object",
"object_type": "red_cube",
"estimated_location": [x, y, z],
"priority": 1
}},
{{
"action": "grasp_object",
"object_type": "red_cube",
"grasp_point": [x, y, z],
"orientation": [qx, qy, qz, qw],
"priority": 2
}},
{{
"action": "transport_object",
"destination": [x, y, z],
"priority": 3
}}
],
"confidence_score": 0.85,
"potential_obstacles": ["chair", "table"],
"alternative_plans": [...]
}}
Ensure the action sequence is executable by a humanoid robot in simulation.
"""
# In a real implementation, this would call the actual LLM
# For now, we'll simulate the response
action_plan = self.simulate_llm_response(command, vision_result)
# Publish action plan
plan_msg = String()
plan_msg.data = action_plan
self.action_plan_pub.publish(plan_msg)
self.get_logger().info(f'Published action plan: {action_plan}')
def simulate_llm_response(self, command, vision_result):
"""Simulate LLM response (in practice, this would call actual LLM)"""
import json
# Parse command to extract object and action
target_object = self.extract_object_from_command(command)
action_type = self.extract_action_from_command(command)
# Create action sequence based on command and vision
action_sequence = []
if action_type == "pick_up" or action_type == "grasp":
# Find target object in vision result
object_info = self.find_object_in_vision(vision_result, target_object)
if object_info:
# Add navigation action
action_sequence.append({
"action": "navigate_to_object",
"object_type": target_object,
"estimated_location": object_info.get("position", [0, 0, 0]),
"priority": 1
})
# Add grasp action
action_sequence.append({
"action": "grasp_object",
"object_type": target_object,
"grasp_point": object_info.get("grasp_point", [0, 0, 0]),
"orientation": [0, 0, 0, 1], # Identity quaternion
"priority": 2
})
# Add transport action (move to default location)
action_sequence.append({
"action": "transport_object",
"destination": [2, 0, 0], # Default drop-off location
"priority": 3
})
elif action_type == "move_to":
# Extract destination from command
destination = self.extract_destination_from_command(command)
if destination:
action_sequence.append({
"action": "navigate_to_location",
"destination": destination,
"priority": 1
})
response = {
"action_sequence": action_sequence,
"confidence_score": 0.85,
"potential_obstacles": ["chair", "table"],
"alternative_plans": []
}
return json.dumps(response)
def extract_object_from_command(self, command):
"""Extract target object from command"""
# Simple keyword extraction (in practice, use NLP)
object_keywords = ["cube", "ball", "box", "cylinder", "sphere", "object"]
for keyword in object_keywords:
if keyword in command:
# Look for color adjectives before the object
words = command.split()
for i, word in enumerate(words):
if word == keyword:
# Check previous word for color
if i > 0:
color = words[i-1]
if color in ["red", "blue", "green", "yellow", "orange", "purple", "pink", "black", "white", "gray"]:
return f"{color}_{keyword}"
return keyword
return "object" # Default
def extract_action_from_command(self, command):
"""Extract action type from command"""
if any(word in command for word in ["pick up", "grasp", "take", "grab"]):
return "pick_up"
elif any(word in command for word in ["go to", "move to", "navigate to"]):
return "move_to"
elif any(word in command for word in ["clean", "organize"]):
return "clean"
return "unknown"
def find_object_in_vision(self, vision_result, target_object):
"""Find object information in vision analysis"""
# This would parse the actual vision result
# For simulation, return mock data
import re
import json
try:
vision_data = json.loads(vision_result)
for obj in vision_data.get("objects", []):
if target_object.lower() in obj.get("name", "").lower():
return {
"position": obj.get("position", [0, 0, 0]),
"grasp_point": obj.get("position", [0, 0, 0]) # Use position as grasp point for now
}
except:
# If not JSON, do simple string matching
if target_object in vision_result:
# Return mock position
return {"position": [1.5, 1.0, 0.1], "grasp_point": [1.5, 1.0, 0.1]}
return None
def extract_destination_from_command(self, command):
"""Extract destination from navigation command"""
# Simple extraction (in practice, use more sophisticated NLP)
if "kitchen" in command:
return [3, 2, 0]
elif "living room" in command:
return [0, 3, 0]
elif "bedroom" in command:
return [-2, 1, 0]
else:
return [1, 1, 0] # Default location
def main(args=None):
rclpy.init(args=args)
node = CapstoneCognitivePlanner()
try:
rclpy.spin(node)
except KeyboardInterrupt:
pass
finally:
node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()
3. Navigation and Path Planning
Components: Nav2, Isaac ROS, obstacle avoidance Requirements:
- Plan paths around static and dynamic obstacles
- Execute navigation with real-time obstacle avoidance
- Integrate with humanoid-specific locomotion constraints
- Provide feedback on navigation status
# config/capstone_navigation_config.yaml
bt_navigator:
ros__parameters:
use_sim_time: true
global_frame: map
robot_base_frame: base_link
odom_topic: /odom
bt_xml_filename: "navigate_w_replanning_and_recovery.xml"
default_server_timeout: 20
enable_groot_monitoring: true
enable_logging: true
enable_scenario_introspection: true
enable_tf_timeout: true
global_frame_to_planner_frame_transforms: ["map", "odom"]
robot_base_frame_to_carrot_frame_transforms: ["base_link", "base_link"]
# Humanoid-specific parameters
goal_checker:
plugin: "nav2_behavior_tree::GoalChecker"
xy_goal_tolerance: 0.3 # Larger tolerance for humanoid precision
yaw_goal_tolerance: 0.5 # Allow more rotational tolerance for bipedal robots
controller_server:
ros__parameters:
use_sim_time: true
controller_frequency: 10.0 # Lower frequency for humanoid stability
min_x_velocity_threshold: 0.05
min_y_velocity_threshold: 0.5
min_theta_velocity_threshold: 0.001
progress_checker_plugin: "progress_checker"
goal_checker_plugin: "goal_checker"
controller_plugins: ["HumanoidMppiController"]
# Humanoid-specific MPPI Controller
HumanoidMppiController:
plugin: "nav2_mppi_controller::MppiController"
time_steps: 20
control_horizon: 10
time_delta: 0.1
discretization: 0.1
# Cost weights for humanoid-specific navigation
cost_obstacles: 3.0
cost_goal_dist: 1.0
cost_path_align: 0.5
cost_goal_angle: 0.2
cost_balance: 4.0 # Penalty for balance violations
cost_foot_placement: 3.0 # Penalty for unstable foot placement
local_costmap:
local_costmap:
ros__parameters:
update_frequency: 10.0
publish_frequency: 5.0
global_frame: odom
robot_base_frame: base_link
use_sim_time: true
rolling_window: true
width: 6 # Larger window for humanoid awareness
height: 6
resolution: 0.05
robot_radius: 0.4 # Larger radius for humanoid
plugins: ["voxel_layer", "inflation_layer"]
inflation_layer:
plugin: "nav2_costmap_2d::InflationLayer"
cost_scaling_factor: 5.0
inflation_radius: 0.8 # Larger inflation for humanoid safety
global_costmap:
global_costmap:
ros__parameters:
update_frequency: 1.0
publish_frequency: 0.5
global_frame: map
robot_base_frame: base_link
use_sim_time: true
robot_radius: 0.4
resolution: 0.05
track_unknown_space: true
plugins: ["static_layer", "obstacle_layer", "inflation_layer"]
obstacle_layer:
plugin: "nav2_costmap_2d::ObstacleLayer"
enabled: true
observation_sources: scan
scan:
topic: /scan
max_obstacle_height: 2.0
clearing: true
marking: true
data_type: "LaserScan"
raytrace_max_range: 5.0 # Longer range for humanoid planning
raytrace_min_range: 0.0
obstacle_max_range: 4.0
obstacle_min_range: 0.0
4. Computer Vision and Object Recognition
Components: Isaac ROS DetectNet, Pose Estimation, Semantic Segmentation Requirements:
- Detect and classify objects in the environment
- Estimate 3D poses of target objects
- Provide semantic segmentation for scene understanding
- Integrate with manipulation planning
# config/capstone_vision_config.yaml
isaac_ros_detectnet:
ros__parameters:
input_topic: "/camera/color/image_rect_color"
output_topic: "/capstone/detections"
model_name: "ssd_mobilenet_v2_coco"
confidence_threshold: 0.7
max_batch_size: 1
input_tensor_names: ["input"]
output_tensor_names: ["scores", "boxes", "classes"]
input_binding_names: ["input"]
output_binding_names: ["scores", "boxes", "classes"]
engine_cache_path: "/tmp/trt_cache/capstone_detectnet.plan"
trt_precision: "FP16"
enable_bbox_hypotheses: true
enable_mask_output: false
mask_post_proc_params: 0.5
bbox_preproc_params: 0.0
bbox_output_format: "CORNER_PAIR"
isaac_ros_semgseg:
ros__parameters:
input_topic: "/camera/color/image_rect_color"
output_topic: "/capstone/segmentation"
model_name: "unet_coco"
confidence_threshold: 0.6
max_batch_size: 1
input_tensor_names: ["input"]
output_tensor_names: ["output"]
input_binding_names: ["input"]
output_binding_names: ["output"]
engine_cache_path: "/tmp/trt_cache/capstone_semseg.plan"
trt_precision: "FP16"
isaac_ros_pose_estimation:
ros__parameters:
input_image_topic: "/camera/color/image_rect_color"
input_camera_info_topic: "/camera/color/camera_info"
output_topic: "/capstone/object_poses"
detection_topic: "/capstone/detections"
model_path: "/models/pose_estimation/model.plan"
object_classes: ["cube", "sphere", "cylinder", "bottle", "cup"]
min_detection_confidence: 0.7
enable_profiling: true
5. Manipulation and Grasping
Components: MoveIt2, Grasp Planning, Isaac ROS Manipulation Requirements:
- Plan stable grasps for detected objects
- Execute manipulation with humanoid-specific constraints
- Integrate with navigation for mobile manipulation
- Provide feedback on grasp success/failure
# config/capstone_manipulation_config.yaml
moveit_cpp:
ros__parameters:
# Planning scene parameters
planning_scene_monitor_options:
name: "planning_scene_monitor"
robot_description: "robot_description"
joint_state_topic: "/joint_states"
attached_collision_object_topic: "/attached_collision_object"
publish_planning_scene_topic: "/publish_planning_scene"
monitored_planning_scene_topic: "/monitored_planning_scene"
wait_for_initial_state_timeout: 10.0
# MoveGroup parameters
move_group:
ros__parameters:
planning_scene_monitor_options:
name: "planning_scene_monitor"
robot_description: "robot_description"
joint_state_topic: "/joint_states"
attached_collision_object_topic: "/attached_collision_object"
planning_options:
plan_only: false
look_around: false
look_around_attempts: 5
max_safe_execution_cost: 10000.0
replan: true
replan_attempts: 5
replan_delay: 0.5
# Humanoid-specific manipulator groups
manipulator_group_name: "humanoid_arm"
end_effector_name: "hand"
pose_reference_frame: "base_link"
# Humanoid-specific constraints
humanoid_manipulation_constraints:
max_velocity_scaling_factor: 0.3 # Slower for stability
max_acceleration_scaling_factor: 0.2
cartesian_position_tolerance: 0.01 # 1cm tolerance
cartesian_orientation_tolerance: 0.1 # 0.1 rad tolerance
joint_tolerance: 0.01
Project Phases and Milestones
Phase 1: System Integration and Basic Functionality (Week 1-2)
- Integrate Isaac Sim environment with humanoid robot model
- Set up basic ROS 2 communication between all components
- Implement simple voice command processing pipeline
- Configure basic navigation with Nav2
- Set up basic object detection pipeline
Phase 2: Component Enhancement (Week 3-4)
- Enhance voice processing with Whisper integration
- Implement LLM cognitive planning for action decomposition
- Configure advanced navigation with obstacle avoidance
- Implement 3D object pose estimation
- Set up basic manipulation capabilities
Phase 3: Integration and Testing (Week 5-6)
- Integrate all components into unified pipeline
- Test voice command � navigation � manipulation sequence
- Implement error handling and recovery behaviors
- Optimize performance and fix bugs
- Conduct comprehensive system testing
Phase 4: Advanced Features and Validation (Week 7-8)
- Implement multi-object manipulation scenarios
- Add dynamic obstacle avoidance during navigation
- Implement semantic understanding of commands
- Conduct user studies and validation
- Prepare final demonstration and documentation
Success Criteria and Evaluation Metrics
Quantitative Metrics
- Task Completion Rate: Percentage of tasks successfully completed
- Target: >80% success rate for basic pick-and-place tasks
- Accuracy Metrics:
- Navigation accuracy:
<0.3mfinal position error - Grasp success rate: >70% for simple objects
- Voice recognition accuracy: >90% for clear commands
- Navigation accuracy:
- Performance Metrics:
- Average task completion time:
<5minutes for basic tasks - System response time:
<2seconds for voice command processing - Navigation speed: 0.5 m/s average in cluttered environments
- Average task completion time:
Qualitative Metrics
- Robustness: Ability to handle unexpected situations gracefully
- Natural Interaction: How intuitive and natural the voice interface feels
- Adaptability: How well the system adapts to new environments/objects
- Safety: How safely the robot operates around humans and obstacles
Risk Assessment and Mitigation
Technical Risks
- Integration Complexity: Multiple complex systems may not integrate smoothly
- Mitigation: Develop modular interfaces, test components individually
- Performance Bottlenecks: GPU/CPU limitations may affect real-time performance
- Mitigation: Profile early, optimize critical paths, use hardware acceleration
- Reliability Issues: Complex system may have frequent failures
- Mitigation: Implement comprehensive error handling, graceful degradation
Schedule Risks
- Dependency Delays: Component development may take longer than expected
- Mitigation: Parallelize development where possible, have backup solutions
- Testing Complexity: System-level testing may reveal difficult-to-fix issues
- Mitigation: Test early and often, use simulation extensively before hardware
Resources and Dependencies
Required Software
- Isaac Sim (latest version)
- Isaac ROS packages
- ROS 2 Humble Hawksbill
- OpenAI Whisper or compatible STT system
- Compatible LLM API access
- Nav2 stack
- MoveIt2
- Docusaurus for documentation
Required Hardware (for real robot testing)
- NVIDIA RTX GPU (recommended: RTX 4080 or better)
- Robot with ROS 2 compatibility (simulated in Isaac Sim initially)
- RGB-D camera
- Microphone array (simulated in Isaac Sim initially)
Hands-On Exercise
- Set up the Isaac Sim environment for the capstone project
- Create a basic humanoid robot model in Isaac Sim
- Implement a simple voice command processing node
- Configure basic navigation using Nav2
- Set up object detection pipeline using Isaac ROS
- Test individual components before integration
- Document any issues or challenges encountered
Example command to test basic voice processing:
# Launch voice processing node
ros2 launch isaac_ros_voice_processing voice_processor.launch.py
# Publish a test audio command (simulated)
ros2 topic pub /audio/microphone std_msgs/String "data: 'pick up the red cube'"
# Monitor the processed command
ros2 topic echo /capstone/voice_command
Summary
This lesson defined the complete scope and requirements for the autonomous humanoid capstone project. The project integrates voice processing, cognitive planning, navigation, computer vision, and manipulation. The next lesson will cover implementing the integration challenges.