🧱 PDDL Modeling for WorkBenchMark

Project P1 — AI for Robotics, Summer Semester 2026 (RWTH Aachen)

Design a PDDL domain for LEGO Duplo assembly, plan with a general-purpose planner, and execute in TAMPanda via DomainBridge.

• 🔗 Benchmark: WorkBenchMark
• 📄 Paper: arXiv:2606.19358

🧭 What This Project Does

WorkBenchMark ships an Assembly-by-Disassembly (ABD) baseline. We model the assembly task symbolically in PDDL and let a classical planner produce the sequence. The generated plan is executed in TAMPanda through DomainBridge and PickPlaceExecutor.

Pipeline: YAML task spec → PDDL problem → planner → action plan → MuJoCo execution

🎯 Scope

What we must deliver, what we aim for if time allows, and what we explicitly leave out.

✅ Minimal Requirements

• Tier 1 (2-brick) and Tier 2 (3–5 brick) vertical stacks
• Ground-truth poses from YAML (no perception)
• MuJoCo simulation via TAMPanda

🚀 Goal

• PDDL domain designed for Tier 3 up front (stud-grid poses, rotation, can-attach / footprint geometry) — no rewrite later
• Tier 3 (3D layouts, half-overlap, multi-column) if time allows
• Compare planning time and plan quality against the ABD baseline

🚫 Not Part of the Project

• Tier 4 complex interlocking assemblies
• Real-robot experiments
• Perception stack (GroundingDINO, SAM, FoundationPose, …)
• Modifying TAMPanda itself

🗺️ Phases

Phase	Goal	Status
1 — Setup	Repo, pyproject.toml, package skeleton, env smoke tests	✅ done
2 — Symbolic layer	lego_domain.pddl, YAML parser, problem generator	🚧 in progress
3 — Execution	DomainBridge wiring, predicate grounding, motion executors	⏳ pending
4 — Evaluation	Batch runner, metrics vs. ABD, failure analysis (Tier 1–2; Tier 3 optional)	⏳ pending

Phase 2 deliverables:

• stud-grid PDDL domain with pick / place / stack
• Tier‑3-ready predicates YAML parser for WorkBenchMark blocks[] specs
• problem generator from initial + goal YAML.

Phase 3 deliverables:

• map PDDL actions to PickPlaceExecutor calls in TAMPanda
• ground symbolic stud/layer/rot poses to continuous YAML poses

Phase 4 deliverables:

• systematic evaluation on Tier 1–2 (N = 20 tasks per tier)
• planning/execution success, time, plan length vs. ABD
• Tier 3 evaluation if implementation of it is completed.
• Failure analysis.

📊 Evaluation Metrics

• ✅ Planning success — planner finds a valid plan
• 🤖 Execution success — all actions succeed in TAMPanda
• ⏱️ Planning time & plan length — vs. ABD baseline where available
• 🔍 Failure analysis — which task structures expose model gaps

📦 Dataset

WorkBenchMark tasks live in the sibling clone [../dataset](../dataset) (WorkBenchMark/dataset):

P1 skips perception, so the YAML→PDDL compiler reads ground_truth/ for both initial layout and goal. It is an explicit project simplification.

⚙️ Setup

Python 3.10+, TAMPanda at ../tampanda, dataset at ../dataset.

cd /work/rleap1/aifr/prak4/Project
source /work/rleap1/aifr/prak4/venv/bin/activate
pip install -e ../tampanda
pip install -e .[dev]
python scripts/check_env.py
pytest