View source for Get started with PDDL on NVIDIA Jetson

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== Project Overview: Symbolic AI Planning on NVIDIA Jetson ==

This project demonstrates the implementation of '''Symbolic AI Planning''' on an NVIDIA Jetson (ARM64) using '''PDDL''' (Planning Domain Definition Language). Unlike traditional machine learning, this approach focuses on deterministic logic and reasoning to solve complex multi-step tasks.

=== 🛠️ Environment Setup ===
Because the Jetson uses an ARM64 architecture, traditional x86 planners (like those found in planutils) often fail. We utilize a native Python approach for maximum compatibility.

* '''Hardware:''' NVIDIA Jetson (Nano/Xavier/Orin)
* '''OS:''' Ubuntu 20.04/22.04 LTS
* '''Language:''' Python 3.10+
* '''Library:''' [https://github.com/aibasel/pyperplan Pyperplan] (Pure Python PDDL Planner)

==== Installation ====
<syntaxhighlight lang="bash">
# Install the planner
pip install pyperplan
</syntaxhighlight>

---

== 📋 The Logistics Domain ==
The domain defines the "physics" of our world. We use `:typing` to categorize objects for the planner.

=== Domain Code (logistics_domain.pddl) ===
<syntaxhighlight lang="pddl">
(define (domain logistics)
  (:requirements :strips :typing)
  
  (:types location truck package)

  (:predicates 
    (at ?obj ?loc)
    (in ?p ?t)
  )

  (:action drive
    :parameters (?t - truck ?from - location ?to - location)
    :precondition (and (at ?t ?from))
    :effect (and (at ?t ?to) (not (at ?t ?from)))
  )

  (:action load
    :parameters (?p - package ?t - truck ?l - location)
    :precondition (and (at ?p ?l) (at ?t ?l))
    :effect (and (in ?p ?t) (not (at ?p ?l)))
  )

  (:action unload
    :parameters (?p - package ?t - truck ?l - location)
    :precondition (and (in ?p ?t) (at ?t ?l))
    :effect (and (at ?p ?l) (not (in ?p ?t)))
  )
)
</syntaxhighlight>

---

== 📍 The Problem Definition ==
The problem defines the specific starting state and the desired goal.

=== Problem Code (logistics_problem.pddl) ===
<syntaxhighlight lang="pddl">
(define (problem delivery-task)
  (:domain logistics)
  
  (:objects 
    warehouse hospital post_office - location
    truck1 - truck
    meds food - package
  )

  (:init 
    (at truck1 warehouse)
    (at meds warehouse)
    (at food warehouse)
  )

  (:goal (and 
    (at meds hospital)
    (at food post_office)
  ))
)
</syntaxhighlight>

---

== 🚀 Execution & Results ==
Run the following command in the Jetson terminal:

<syntaxhighlight lang="bash">
pyperplan logistics_domain.pddl logistics_problem.pddl
</syntaxhighlight>

=== Expected Output ===
The planner uses Breadth-First Search (BFS) to find the shortest path.
* '''Search Time:''' ~0.00092s
* '''Nodes Expanded:''' 42
* '''Plan Length:''' 6 steps

The generated plan is saved to <code>logistics_problem.pddl.soln</code>.

---

== ⚠️ Troubleshooting & Lessons Learned ==
* '''Architecture Conflicts:''' Avoid using <code>planutils</code> or <code>up-tamer</code> on Jetson as they rely on x86 pre-compiled binaries or Apptainer images that lack ARM64 support.
* '''Typing Requirement:''' Ensure <code>(:requirements :typing)</code> is declared in the domain if using typed objects (e.g., <code>- location</code>), otherwise, Pyperplan will throw a <code>SemanticError</code>.
* '''Validation:''' If <code>validate</code> is missing from PATH, the plan still runs but cannot be formally verified by the software.

[[Category:Robotics]] [[Category:Artificial Intelligence]] [[Category:Jetson]]