Logic-Geometric Programming: An Optimization-Based Approach to Combined Task and Motion Planning

Authors: Marc Toussaint

IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental 5 Experiments We evaluated the approach on the problem of creating a stable construction from a random set of assorted boards and blocks, maximizing its height.
Researcher Affiliation Academia Marc Toussaint Machine Learning & Robotics Lab University Stuttgart, Germany marc.toussaint@ipvs.uni-stuttgart.de
Pseudocode No The paper describes algorithmic steps but does not include any structured pseudocode or clearly labeled algorithm blocks.
Open Source Code No The paper does not provide any explicit statement about releasing source code or a link to a code repository for the methodology described.
Open Datasets No The paper mentions using a "random set of assorted boards and blocks" and "random problem instances" for experiments, but it does not provide any concrete access information (link, DOI, repository, or citation) for a publicly available or open dataset.
Dataset Splits No The paper does not provide explicit details on training, validation, or test dataset splits, percentages, or sample counts.
Hardware Specification Yes Figure 3(a) displays data on the run times on a 2.8GHz Dual Core laptop
Software Dependencies No The paper mentions "our k-order motion optimization framework (KOMO) [Toussaint, 2014]" but does not provide specific version numbers for KOMO or any other software dependencies.
Experiment Setup Yes We abstracted the robot as a 3Do F arm mounted on a floating base. ... The concrete switch constraints hswitch, gswitch used in the experiment concerned non-collision (inequality), equality of grasp frame with object frame, and (in case of the last manipulation of an object) equality of the place pose with the final pose. ... When only optimizing over the keyframes (level 2) we choose a path discretization that only includes the keyframes; for the full path optimization we include 20 time steps between keyframes initialize the path optimization with an interpolation of the previously optimized keyframes.