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Watch out! There may be a Human. Addressing Invisible Humans in Social Navigation

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Phani Teja Singamaneni, Anthony Favier, Rachid Alami

Robotics · Submitted 22 Nov 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Finally, the effectiveness of the proposed methodology is shown through several simulated and real-world experiments.

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arXiv ID2211.12216 (version 1)
Pith IDW2HVZEPW, Pith's permanent record of this paper and its review
CitationAnthony Favier, Phani Teja Singamaneni, Rachid Alami. "Watch out! There may be a Human. Addressing Invisible Humans in Social Navigation." arXiv:2211.12216 (2022). https://pith.science/paper/W2HVZEPW
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,456 input tokens.

What the abstract itself states, as probabilities: claims first 0.13 · states limitation 0.10 · human participants 0.16 · controlled comparison 0.07 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.13 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Current approaches in human-aware or social robot navigation address the humans that are visible to the robot. However, it is also important to address the possible emergences of humans to avoid shocks or surprises to humans and erratic behavior of the robot planner. In this paper, we propose a novel approach to detect and address these human emergences called `invisible humans'. We determine the places from which a human, currently not visible to the robot, can appear suddenly and then adapt the path and speed of the robot with the anticipation of potential collisions. This is done while still considering and adapting humans present in the robot's field of view. We also show how this detection can be exploited to identify and address the doorways or narrow passages. Finally, the effectiveness of the proposed methodology is shown through several simulated and real-world experiments.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Current approaches in human-aware or social robot navigation address the humans that are visible to the robot. background 0.98 0.04 0.02 asserted 1.00
s1 However, it is also important to address the possible emergences of humans to avoid shocks or surprises to humans and erratic behavior of the robot planner. gap 0.75 0.07 0.06 asserted 0.97
s2 In this paper, we propose a novel approach to detect and address these human emergences called `invisible humans'. contribution 1.00 0.72 0.14 asserted 0.99
s3 We determine the places from which a human, currently not visible to the robot, can appear suddenly and then adapt the path and speed of the robot with the anticipation of potential collisions. method 0.95 0.92 0.15 asserted 1.00
s4 This is done while still considering and adapting humans present in the robot's field of view. method 0.90 0.81 0.05 asserted 0.99
s5 We also show how this detection can be exploited to identify and address the doorways or narrow passages. contribution 0.84 0.88 0.06 asserted 0.94
s6 Finally, the effectiveness of the proposed methodology is shown through several simulated and real-world experiments. result 0.72 0.94 0.45 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.93.

What was examined, and what was not

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  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Current approaches in human-aware or social robot navigation address the humans that are visible to the robot. However, it is also important to address the possible emergences of humans to avoid shocks or surprises to humans and erratic behavior of the robot planner. In this paper, we propose a novel approach to detect and address these human emergences called `invisible humans'. We determine the places from which a human, currently not visible to the robot, can appear suddenly and then adapt the path and speed of the robot with the anticipation of potential collisions. This is done while still considering and adapting humans present in the robot's field of view. We also show how this detection can be exploited to identify and address the doorways or narrow passages. Finally, the effectiveness of the proposed methodology is shown through several simulated and real-world experiments.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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