Peteris Racinskis, Oskars Vismanis, Toms Eduards Zinars, Janis Arents, Modris Greitans. Correction to: Towards Open-Set NLP-Based Multi-Level Planning for Robotic Tasks. Applied Sciences (Switzerland), 15(5), MDPI, 2025.
Bibtex citation:
Bibtex citation:
@article{19982_2025,
author = {Peteris Racinskis and Oskars Vismanis and Toms Eduards Zinars and Janis Arents and Modris Greitans},
title = {Correction to: Towards Open-Set NLP-Based Multi-Level Planning for Robotic Tasks},
journal = {Applied Sciences (Switzerland)},
volume = {15},
issue = {5},
publisher = {MDPI},
year = {2025}
}
author = {Peteris Racinskis and Oskars Vismanis and Toms Eduards Zinars and Janis Arents and Modris Greitans},
title = {Correction to: Towards Open-Set NLP-Based Multi-Level Planning for Robotic Tasks},
journal = {Applied Sciences (Switzerland)},
volume = {15},
issue = {5},
publisher = {MDPI},
year = {2025}
}
Abstract: In the original publication [1], there was a mistake in Table 1 as published. The column data in the table are reversed. The corrected Table 1 appears below. The numbers in the Abstract referring to Table 1 were mismatched. A correction has been made to the Abstract. From “while the semantic maps exhibit maximum recall rates of 94.69% and 92.29% for the 2.5d and 3d versions, respectively.” To “while the semantic maps exhibit maximum recall rates of 92.29% and 94.69% for the 2.5d and 3d versions, respectively.” The statements in the Discussion section citing Table 1 were mismatched. A correction has been made to the Discussion Section, Paragraph 5: From “For the static depth map, using ternary embedding values appears to paradoxically result in slightly improved recall—though this peculiarity is likely to disappear with larger and more varied data sets. With the vectree map, the same output set was produced by 32-bit and 8-bit embeddings, with two otherwise recalled observations being missed (one each of “white bottle” and “energy drink bottle”) and one otherwise missed observation being recalled (“pink foam”).” To “For the vectree map, using ternary-embedding values appears to paradoxically result in slightly improved recall—though this peculiarity is likely to disappear with larger and more varied datasets. With the static depth map, the same output set was produced by 32-bit and 8-bit embeddings, with two otherwise recalled observations being missed (one each of “white bottle” and “energy drink bottle”) and one otherwise missed observation being recalled (“pink foam”) with ternary embeddings.” The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated. © 2025 Elsevier B.V., All rights reserved.