Correction: Racinskis et al. Towards Open-Set NLP-Based Multi-Level Planning for Robotic Tasks. Appl. Sci. 2024, 14, 10717

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. Object queries and recall rates according to map type and vector quantization level.

Query Text	Recall %						Seen n Times
	Static Depth Map			VectorOcTree Map
	Ternary	Byte	Float32	Ternary	Byte	Float32
protective eyewear	75	75	75	75	100	75	4
pink foam	100	66.67	66.67	100	100	100	3
grey key	100	100	100	100	100	100	1
realsense box	100	100	100	100	100	100	2
wrench	100	100	100	100	40	100	5
computer mouse	100	100	100	100	100	100	5
white bottle	40	60	60	60	60	60	5
multimeter	100	100	100	100	66.67	66.67	3
blue stapler	100	100	100	100	100	100	3
toy car	100	100	100	80	60	100	5
red lego	100	100	100	100	100	100	3
scotch tape	100	100	100	100	100	100	5
pair of scissors	100	100	100	100	100	100	4
energy drink bottle	66.67	100	100	100	100	100	3
tape measure	75	75	75	100	75	100	4
blue lego	100	100	100	100	100	100	1
mean recall	91.04	92.29	92.29	94.69	87.6	93.85	Σ = 56

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.