Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
KABouM: Knowledge-Level Action and Bounding Geometry Motion Planner
Authors: Andre Gaschler, Ronald P. A. Petrick, Oussama Khatib, Alois Knoll
JAIR 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on a wide set of problems using real robots, including tasks with multiple manipulators, sensing and branched plans, and mobile manipulation. |
| Researcher Affiliation | Academia | Andre Gaschler EMAIL fortiss An-Institut Technische Universit at M unchen Munich, Germany Ronald P. A. Petrick EMAIL Department of Computer Science, Heriot-Watt University Edinburgh, United Kingdom Oussama Khatib EMAIL Artificial Intelligence Laboratory, Stanford University Stanford, USA Alois Knoll EMAIL Institut f ur Informatik, Technische Universit at M unchen Garching b. M unchen, Germany |
| Pseudocode | Yes | Algorithm 1 Bounded edge contraction point optimisation Input: Neighbouring planes P of an edge Output: Edge contraction point v and its cost... Algorithm 2 Bounding mesh generation with iterative edge contraction Input: Mesh m, tolerance ε Output: Bounding mesh m m |
| Open Source Code | No | The domain definitions of the evaluated scenarios will be made available on the first author’s website http://www.andre-gaschler.de/ at the time of publication. |
| Open Datasets | No | In the Remove n Objects scenario,... problem instances were generated systematically: random locations were evenly distributed over a rectangular table area until a non-colliding set was found, locations in a line were generated with a defined starting position and increment. |
| Dataset Splits | No | problem instances were generated systematically: random locations were evenly distributed over a rectangular table area until a non-colliding set was found, locations in a line were generated with a defined starting position and increment. |
| Hardware Specification | Yes | All plans were generated on desktop computers with a dual-core 2.8 GHz processor and 8 GB of memory. |
| Software Dependencies | No | High-level planning capabilities in KABou M are provided by the Planning with Knowledge and Sensing system (PKS, Petrick & Bacchus, 2002, 2004), an existing general-purpose planner |
| Experiment Setup | Yes | In the Bartender scenario, the robot CAD models (86,746 vertices) are first simplified to bounding meshes of 9,030 vertices, and then decomposed into only 22 convex bodies with a total of 963 vertices, all at a bounded geometric error ε < 0.03 m. |