Perception to Planning Flow

Overview

The perception to planning flow represents the critical transition from environmental understanding to action generation in the autonomous humanoid pipeline. This flow takes the environmental state established through perception and generates executable plans to achieve the goals specified by the voice command. It bridges the gap between the current state of the world and the desired future state.

System Architecture

Flow Diagram

Environment State → Task Decomposition → Feasibility Analysis → Path Planning → Manipulation Planning → Plan Validation → Action Sequence → Plan Execution

Component Integration

State Interpreter: Converts environmental state to planning representation
Task Planner: Decomposes high-level goals into executable subtasks
Path Planner: Generates navigation plans through the environment
Manipulation Planner: Creates plans for object interaction
Plan Validator: Ensures plan safety and feasibility

Task Decomposition

Hierarchical Planning

High-Level Goals: Overall task objectives from voice command
Subtask Generation: Breaking down complex tasks into simpler actions
Dependency Analysis: Identifying task ordering and constraints
Resource Allocation: Assigning robot capabilities to subtasks

Example Decomposition

Goal: "Bring me the red cup from the kitchen table"
Decomposed Tasks:
1. Navigate to kitchen
2. Locate red cup on table
3. Plan approach trajectory to cup
4. Execute grasping motion
5. Navigate to user location
6. Execute placement motion

Planning Representations

Symbolic Planning: High-level task representations
Geometric Planning: Spatial relationships and configurations
Temporal Planning: Time-based task scheduling
Probabilistic Planning: Uncertainty-aware planning

Feasibility Analysis

Environmental Constraints

Collision Detection: Identifying obstacles and collision risks
Kinematic Constraints: Robot joint limits and reachability
Dynamic Constraints: Balance and stability requirements
Resource Constraints: Battery, computation, and time limits

Robot Capabilities Assessment

Manipulation Abilities: Grasping and manipulation capabilities
Locomotion Abilities: Walking and navigation capabilities
Sensing Abilities: Perception and localization capabilities
Cognitive Abilities: Reasoning and decision-making capabilities

Risk Assessment

Safety Analysis: Potential harm to robot, environment, or humans
Failure Probability: Likelihood of plan execution failure
Recovery Options: Available strategies for handling failures
Contingency Planning: Alternative plans for different scenarios

Path Planning Integration

Global Path Planning

Topological Maps: High-level navigation between locations
Occupancy Grids: Detailed obstacle information for navigation
Semantic Maps: Object and region information for navigation
Dynamic Obstacles: Planning around moving obstacles

Local Path Planning

Obstacle Avoidance: Real-time collision avoidance
Dynamic Window Approach: Velocity-based local planning
Potential Fields: Gradient-based obstacle avoidance
Reactive Planning: Immediate response to environmental changes

Walking Planning: Bipedal locomotion planning
Climbing Planning: Stair and step navigation
Human-Aware Navigation: Socially-aware navigation
Energy-Efficient Navigation: Optimizing for power consumption

Manipulation Planning

Grasp Planning

Object Properties: Shape, size, weight, and material properties
Grasp Synthesis: Generating stable and task-appropriate grasps
Approach Planning: Planning safe approach trajectories
Grasp Validation: Ensuring grasp feasibility and stability

Task-Specific Manipulation

Pouring Actions: Planning fluid manipulation
Assembly Tasks: Planning multi-object manipulation
Tool Use: Planning manipulation with tools
Human Handover: Planning safe object transfer to humans

Multi-Arm Coordination

Bimanual Planning: Coordinating two arms for complex tasks
Whole-Body Planning: Integrating manipulation with locomotion
Balance Maintenance: Maintaining stability during manipulation
Workspace Optimization: Maximizing manipulation workspace

Plan Validation and Optimization

Safety Validation

Collision Checking: Ensuring plan trajectories are collision-free
Stability Analysis: Verifying balance during planned motions
Joint Limit Checking: Ensuring movements stay within safe limits
Force Limit Checking: Preventing excessive forces on robot

Performance Optimization

Time Optimization: Minimizing task completion time
Energy Optimization: Minimizing energy consumption
Smoothness: Ensuring smooth, jerk-free motions
Efficiency: Optimizing for robot-specific constraints

Robustness Analysis

Uncertainty Handling: Planning under environmental uncertainty
Sensor Noise: Accounting for perception inaccuracies
Actuator Noise: Accounting for control imprecisions
Model Errors: Handling discrepancies between model and reality

Implementation Example

Task Planning Node

class TaskPlanningNode(Node):
    def __init__(self):
        super().__init__('task_planning_node')
        self.state_sub = self.create_subscription(
            EnvironmentState, 'environment_state', self.state_callback, 10)
        self.goal_sub = self.create_subscription(
            TaskGoal, 'task_goal', self.goal_callback, 10)
        self.plan_pub = self.create_publisher(
            ActionPlan, 'action_plan', 10)

        self.current_state = None
        self.current_goal = None

    def state_callback(self, msg):
        self.current_state = msg
        if self.current_goal:
            self.generate_plan()

    def goal_callback(self, msg):
        self.current_goal = msg
        if self.current_state:
            self.generate_plan()

    def generate_plan(self):
        # Decompose task into subtasks
        subtasks = self.decompose_task(self.current_goal, self.current_state)

        # Generate detailed action plan
        plan = ActionPlan()
        for subtask in subtasks:
            action_sequence = self.plan_subtask(subtask, self.current_state)
            plan.actions.extend(action_sequence)

        # Validate and optimize plan
        validated_plan = self.validate_and_optimize_plan(plan)

        # Publish plan
        self.plan_pub.publish(validated_plan)

    def decompose_task(self, goal, state):
        # Use LLM or rule-based decomposition
        # Return list of subtasks
        pass

    def plan_subtask(self, subtask, state):
        # Generate specific actions for subtask
        # Integrate navigation and manipulation planning
        pass

    def validate_and_optimize_plan(self, plan):
        # Check safety, feasibility, and optimize
        # Return validated plan
        pass

Integration with Other Modules

Navigation2 Stack: Using MoveBaseFlex for navigation planning
MoveIt! Integration: Using MoveIt! for manipulation planning
TF2 Coordination: Managing coordinate frame transformations
Action Server Integration: Using ROS 2 action servers for long-running tasks

Isaac ROS Integration

GPU-Accelerated Planning: Leveraging Isaac ROS for planning
Perception Integration: Using Isaac ROS perception outputs
Simulation Validation: Testing plans in Isaac Sim before execution
Real-time Optimization: GPU-accelerated plan optimization

Challenges and Solutions

Computational Complexity

Challenge: Planning complex tasks in real-time
Solution: Hierarchical planning and parallel processing
Solution: Pre-computed motion primitives and templates
Solution: Approximate planning with guarantees

Uncertainty Management

Challenge: Planning under environmental and sensing uncertainty
Solution: Probabilistic planning and belief space planning
Solution: Replanning when new information becomes available
Solution: Robust planning with uncertainty bounds

Challenge: Coordinating navigation and manipulation planning
Solution: Whole-body planning approaches
Solution: Hierarchical task and motion planning (HTMP)
Solution: Integrated perception-action loops

Performance Metrics

Planning Quality

Plan Optimality: How close the plan is to optimal
Success Rate: Percentage of plans that execute successfully
Feasibility: Percentage of plans that are physically feasible
Safety Rate: Percentage of plans that are safe to execute

Computational Performance

Planning Time: Time to generate a complete plan
Memory Usage: Memory required for planning process
CPU Utilization: Computational resources used
Real-time Factor: Planning speed relative to real-time execution

Robustness

Replanning Frequency: How often plans need to be regenerated
Failure Recovery: Ability to handle plan execution failures
Adaptability: Ability to adapt plans to changing conditions
Uncertainty Handling: Performance under uncertain conditions

Future Enhancements

Advanced Planning Techniques

Learning-Based Planning: Using ML to improve planning efficiency
Neuro-Symbolic Planning: Combining neural and symbolic approaches
Multi-Robot Planning: Coordinating multiple robots for tasks
Human-Robot Planning: Including humans in planning processes

Scalability Improvements

Distributed Planning: Scaling planning across multiple processors
Cloud Integration: Leveraging cloud resources for complex planning
Hierarchical Optimization: Multi-level optimization approaches
Real-time Adaptation: Dynamic plan adjustment during execution

The next section will explore the planning to navigation flow, where action plans are executed through the robot's locomotion system.