What is Task Planning (Robotics)?
Task Planning (Robotics) is the AI discipline of determining the optimal sequence of actions a robot should perform to achieve a given goal. It involves breaking complex objectives into ordered steps, allocating resources, handling dependencies, and adapting plans when unexpected situations arise during execution.
What is Task Planning in Robotics?
Task Planning in robotics is the process by which an AI system determines what sequence of actions a robot should execute to accomplish a specified goal. If motor control answers "how should the robot move?" and vision answers "what does the robot see?", task planning answers the higher-level question: "what should the robot do and in what order?"
Consider a simple example: a robot tasked with assembling a piece of furniture. The task planner must determine that legs should be attached before the table is flipped upright, that screws must be inserted before tightening, and that the work surface must be clear before beginning. This kind of sequential reasoning, obvious to humans, requires sophisticated AI algorithms when performed by machines.
How Task Planning Works
Task planning in robotics draws on decades of AI research in automated planning and has evolved through several approaches:
- Classical planning: The system represents the world as a set of states and actions. Each action has preconditions (what must be true before the action can execute) and effects (what changes after execution). The planner searches for a sequence of actions that transforms the initial state into the goal state.
- Hierarchical task networks: Complex tasks are decomposed into simpler subtasks, which are further decomposed until reaching primitive actions the robot can execute directly. This mirrors how humans break down complex tasks into manageable steps.
- Temporal planning: Extends classical planning to handle time constraints, concurrent actions, and deadlines. Essential for real-world applications where multiple operations must be coordinated and completed within time windows.
- Contingency planning: Plans include branches for handling unexpected situations, such as a missing part or a failed grasp attempt. The robot can switch to alternative action sequences without requiring a complete replan.
- AI-driven adaptive planning: Modern systems use machine learning to improve planning based on experience, learning which action sequences are most likely to succeed and adapting plans in real time based on sensor feedback.
Task Planning Versus Motion Planning
It is important to distinguish task planning from motion planning, as both are essential but operate at different levels:
- Task planning determines what to do: "Pick up bolt, insert into hole, tighten." It deals with the logical sequence of operations.
- Motion planning determines how to move: "Move arm along this specific path to reach the bolt while avoiding obstacles." It deals with physical trajectories.
A complete robotic system needs both. The task planner generates a sequence of high-level actions, and the motion planner generates the detailed movements needed to execute each action.
Business Applications
Manufacturing Assembly
Robots assembling complex products must follow precise sequences where the order of operations matters. Task planners manage these sequences, handling variations in product configurations and adapting when components are not in expected positions.
Warehouse Order Fulfilment
Task planning optimises the sequence in which a robot picks items for an order, minimising travel time, managing bin capacity, and coordinating with other robots operating in the same space.
Process Automation
In chemical, pharmaceutical, and food manufacturing, task planners manage multi-step processes where timing, sequencing, and environmental conditions must be carefully controlled.
Construction and Maintenance
Robots performing maintenance tasks or assisting in construction must plan sequences of inspection, disassembly, repair, and reassembly steps, adapting to the specific conditions they discover.
Multi-Robot Coordination
When multiple robots work together on a shared task, task planning becomes even more critical. The planner must assign subtasks to different robots, manage dependencies between their activities, and prevent conflicts.
Task Planning in Southeast Asian Industry
As robotic automation grows across Southeast Asia, task planning becomes increasingly important for several reasons:
- Product variety: Southeast Asian manufacturers often produce a wider variety of products in smaller batches than mass-production facilities, requiring more flexible task planning that can adapt to different products and configurations.
- Mixed automation: Many regional factories combine automated and manual workstations. Task planners must coordinate robot activities with human workers, managing handoffs and ensuring safety.
- Supply chain variability: Variability in incoming components and materials requires task planning systems that can adapt to what is available rather than failing when conditions do not match expectations.
- Scaling automation gradually: Companies adding robots incrementally need task planning systems that can grow from managing a single robot to coordinating multiple machines as automation expands.
Emerging Approaches
Large Language Models for Task Planning
Researchers are exploring how large language models can generate robot task plans from natural language instructions, such as telling a robot to "clean up the table and set it for dinner." Early results are promising for generating initial plan structures that can be refined by traditional planning algorithms.
Learning from Demonstration
Instead of explicitly programming task sequences, robots observe human workers performing tasks and learn the sequence, timing, and contingency strategies. This approach is particularly valuable for tasks that are difficult to specify formally.
Digital Twin Planning
Task plans are developed and tested in virtual simulations of the factory or workspace before deployment, reducing risk and allowing rapid iteration on plan strategies.
Considerations for Implementation
Businesses implementing robotic task planning should consider:
- Start with well-structured tasks: Begin with processes that have clear sequences and limited variability before tackling open-ended planning challenges
- Define error handling strategies: Specify what the robot should do when things do not go according to plan, such as a missing part or a failed operation
- Integrate with existing systems: Task planners work best when they can access information from manufacturing execution systems, inventory databases, and sensor networks
- Plan for human interaction: Design task plans that include clear points for human oversight, intervention, and approval where needed
- Monitor and optimise: Use execution data to identify bottlenecks, failures, and opportunities for plan improvement
Task planning is the intelligence layer that transforms robots from single-function machines into flexible automation systems capable of handling complex, multi-step operations. For business leaders evaluating robotic automation, task planning capability determines how much human supervision and intervention a robotic system requires, and therefore how much operational cost it actually saves.
A robot with sophisticated task planning can handle product changeovers, adapt to missing or mispositioned components, coordinate with other machines, and recover from errors with minimal human intervention. A robot without effective task planning requires constant human oversight and reprogramming for every variation, significantly reducing the expected return on investment.
For Southeast Asian manufacturers operating in dynamic environments with diverse product mixes and evolving production requirements, task planning flexibility is particularly valuable. The ability of a robotic system to adapt its behaviour without extensive reprogramming means shorter changeover times, less production downtime, and more realistic automation for the high-variety, lower-volume production runs that characterise much of the region's manufacturing landscape.
- Evaluate how easily the task planning system can be reprogrammed or reconfigured when products or processes change. High flexibility reduces total cost of ownership significantly.
- Ensure the task planning system includes robust error recovery. In real production environments, things go wrong frequently, and the system must handle exceptions gracefully rather than stopping and waiting for human intervention.
- Consider whether you need deterministic planning (same plan every time for the same conditions) or adaptive planning (plan varies based on learned performance data). Manufacturing typically needs deterministic behaviour for traceability, while logistics benefits from adaptive optimisation.
- Integrate task planning with your production scheduling and inventory systems. A task planner that knows what materials are available and what orders are due can make better sequencing decisions.
- Plan for multi-robot coordination if you expect to add more robots over time. Retrofitting coordination capabilities is more expensive than designing for it from the start.
- Invest in simulation tools that allow you to test task plans virtually before deploying them to physical robots. This reduces commissioning time and risk of costly errors.
- Document task plans and their rationale. As your robotic operations grow, institutional knowledge of why tasks are sequenced in specific ways becomes valuable for troubleshooting and optimisation.
Frequently Asked Questions
How does task planning handle unexpected situations during robot operation?
Modern task planning systems use contingency planning and real-time replanning to handle unexpected events. The system maintains a model of expected conditions at each step and monitors actual conditions through sensors. When a discrepancy is detected, such as a part not being in the expected location or a grasping attempt failing, the planner can invoke pre-defined recovery actions, substitute alternative methods, or generate a new plan from the current state. The sophistication of error handling varies by system, from simple retry logic to full replanning capabilities. For business operations, it is important to define the scope of situations the system should handle autonomously versus when it should escalate to human operators.
Can we use task planning to coordinate multiple robots working together?
Yes, multi-robot task planning is an active area of both research and commercial deployment. These systems decompose a larger task into subtasks, assign them to available robots based on capability and proximity, manage dependencies and timing between robot activities, and prevent conflicts such as two robots trying to access the same workspace simultaneously. Commercial multi-robot systems for warehousing from companies like Amazon Robotics, Geek+, and Hai Robotics use sophisticated task planning to coordinate fleets of hundreds of mobile robots. For manufacturing, multi-robot task planning coordinates activities like one robot holding a part while another welds it.
More Questions
The effort varies significantly depending on the approach and complexity. Traditional task programming requires engineers to explicitly define every action, sequence, and contingency, which can take weeks to months for complex applications. Modern approaches using intuitive programming interfaces, learning from demonstration, and pre-built task templates can reduce setup time to days or weeks. Some platforms allow operators to configure task sequences through graphical interfaces without traditional programming skills. The trend is toward easier, faster task definition, but complex multi-step processes with many contingencies still require significant engineering effort, particularly when safety-critical operations are involved.
Need help implementing Task Planning (Robotics)?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how task planning (robotics) fits into your AI roadmap.