Feasibility-as-a-Service: A Constructor-Theoretic Approach to Ensuring Physical Validity in AI-Powered Robotics
Assertions about probabilities do not refer to the physical world they don't assert anything about the physical world. - David Deutsch
Abstract
As AI-driven robotics continues to evolve, one of the fundamental challenges is ensuring that AI-generated plans and actions are physically feasible. Traditional approaches rely on probabilistic heuristics, reinforcement learning, and simulation-dependent validation, none of which deterministically verify task feasibility within real-world constraints. This paper introduces Feasibility-as-a-Service (FaaS)—a novel framework grounded in Constructor Theory that enables real-time feasibility validation before execution. By shifting from probability-based modeling to counterfactual reasoning, this approach bridges the gap between high-level AI-driven task generation and real-world robotic execution. We analyze the high variability in robotic settings, examine the limitations of incumbent approaches, and propose a constraint-driven feasibility validation system that ensures physically valid, scalable, and adaptable robotic autonomy.
1. Introduction
The integration of foundation models into robotics marks a paradigm shift in autonomous systems, allowing robots to learn and generate high-level plans without extensive pre-programming. However, a critical challenge remains—AI-generated plans are not inherently physically grounded. Unlike structured digital domains such as language and image processing, robotic decision-making must conform to fundamental physical laws. The absence of explicit feasibility validation in AI-driven robotics leads to failure-prone execution, operational inefficiencies, and safety risks.
1.1 The Job to Be Done: Enabling Constraint-Driven Robotic Adaptability
For robotics to achieve safe and reliable autonomy, the key Job to Be Done (JTBD) is:
Enable robots to dynamically validate the feasibility of AI-generated tasks before execution, ensuring adherence to fundamental physical constraints in real time.
This principle-driven framework serves multiple stakeholders:
Industrial Robotics – Reduces failure rates and enhances efficiency by prevalidating task execution.
Autonomous Vehicles – Ensures real-time safety by preventing impossible actions before execution.
General-Purpose AI Robotics – Enables cross-platform generalization, eliminating redundant retraining for different robotic embodiments.
To bridge the gap between AI-generated task planning and real-world execution, we propose Feasibility-as-a-Service (FaaS)—a Constructor-Theoretic framework that enables real-time feasibility validation using counterfactual reasoning.
2. Limitations of Incumbent Approaches
2.1 Empirical Trial-and-Error
Many robotic systems attempt actions first and learn from failure. While feasible in controlled settings, this approach does not scale to real-world deployments. Failure-based learning risks damaging hardware, increasing operational downtime, and introducing safety hazards.
Example Failure: A warehouse robot attempts to lift a pallet beyond its torque limit, causing mechanical failure.
2.2 Probabilistic Heuristics & Reinforcement Learning
Many AI-driven robots use probability-based feasibility checks (e.g., reinforcement learning), estimating success rates instead of deterministically verifying task feasibility.
Key Issue: A self-driving car estimating a 90% chance of successfully navigating an intersection does not prevent catastrophic failure in the remaining 10% of cases.
2.3 Simulation-Based Feasibility Checking
Simulations (e.g., Gazebo, Isaac Sim) attempt to validate feasibility but suffer from the sim-to-real gap—discrepancies between simulated environments and real-world physics.
Key Weakness: Simulated robotic arms trained in idealized friction conditions fail in real-world industrial settings due to surface inconsistencies and sensor noise.
3. Proposed Solution: Feasibility-as-a-Service (FaaS)
3.1 Overview of FaaS
FaaS is a validation layer that ensures all AI-generated robotic plans are physically feasible before execution.
Key Components:
Feasibility Engine – Validates robotic plans against real-world constraints.
Counterfactual Constraint Library – A modular database of physical constraints (e.g., torque limits, energy expenditure).
Edge Deployment & Adaptation – Enables real-time feasibility validation in dynamic environments.
3.2 Constructor-Theoretic Approach
Drawing from Constructor Theory, FaaS redefines feasibility validation using counterfactual reasoning—determining whether a task is possible before execution.
Key Principles:
Counterfactual Task Validation – Robots verify task feasibility without needing to attempt and fail.
Transformation-Oriented Feasibility Checking – Tasks are modeled as physical transformations, ensuring adherence to fundamental constraints.
Universality & Scalability – Cross-platform feasibility validation, reducing dependence on task-specific training.
3.3 Open-Source Counterfactual Constraint Library
A shared repository that enables:
Predefined Constraints – Includes physics-based limits (e.g., Friction-Model-0.3, ArmTorqueLimit-10Nm).
Community Contributions – Researchers can submit new models (e.g., improved friction handling).
Real-Time Adaptation – Dynamically updates constraints using sensor feedback.
4. Case Study: Feasibility Validation in Humanoid Robotics
4.1 Example: Apptronik Humanoid in a Warehouse
Scenario:
A warehouse operator issues a command to a humanoid robot:
"Move those 25 kg crates to Shelf 3."
FaaS ensures:
✅ Feasibility Engine checks torque, balance, and grasp constraints before execution.
✅ If feasible, the task is executed.
✅ If infeasible, FaaS suggests modifications (e.g., "Load one crate at a time" or "Use both arms").
Impact:
Failure Prevention – Prevents robots from attempting infeasible actions.
Enhanced Safety – Reduces operational risk by ensuring physical validity.
Simplified User Experience – Natural language task execution without feasibility concerns.
5. Conclusion & Future Implications
Feasibility-as-a-Service (FaaS) represents a paradigm shift in robotic feasibility validation, eliminating failure-prone probabilistic decision-making in favor of deterministic, principle-based validation.
By leveraging Constructor Theory and counterfactual reasoning, FaaS:
✅ Ensures real-time feasibility validation instead of trial-and-error failure detection.
✅ Creates a universal robotic feasibility framework, reducing reliance on retraining.
✅ Establishes a standardized validation layer, enabling safer, more scalable robotic autonomy.
This research lays the foundation for a universal robotic OS, where robots across industries can dynamically validate physical feasibility—ensuring a new era of safe, adaptable, and autonomous robotics
.
References
Constructor Theory
https://www.constructortheory.org/
Marletto, C., The Science of Can and Can’t: A Physicist’s Journey Through the Land of Counterfactuals.
https://www.chiaramarletto.com/books/the-science-of-can-and-cant/
Foundation Models in Robotics: Applications, Challenges, and the Future
https://arxiv.org/html/2312.07843v1/#S4
David Deutsch on Physics Without Probability