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Cited by 341

The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.

ANYmal - toward legged robots for harsh environments

Open access
Hutter, M., Gehring, C., Lauber, A., Gunther, F., Bellicoso, C. D., Tsounis, V., Fankhauser, P., Diethelm, R., Bachmann, S., Bloesch, M., Kolvenbach, H., Bjelonic, M., Isler, L., Meyer, K.·2017·Advanced Robotics

Abstract

Graphical Abstract

Overview

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Integrity signals

Registry

Crossref · 10.1080/01691864.2017.1378591

Cites

30 of 30 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

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Self-check

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Citations

Cites

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Cited by

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Metadata

Authors
Hutter, M., Gehring, C., Lauber, A., Gunther, F., Bellicoso, C. D., Tsounis, V., Fankhauser, P., Diethelm, R., Bachmann, S., Bloesch, M., Kolvenbach, H., Bjelonic, M., Isler, L., & Meyer, K.
Venue
Advanced Robotics
Published
2017-09-02
Volume
31
Issue
17
Pages
918-931
ISSN
  • 0169-1864 (ISSN-L)
  • 1568-5535
OpenAlex
W2764255492
Cited by
341
References
30
FWCI
9.20
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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This page
CoChat. (2026). Citation check: ANYmal - toward legged robots for harsh environments. https://app.cochat.ai/papers/10.1080/01691864.2017.1378591
The paper
Hutter, M., Gehring, C., Lauber, A., Gunther, F., Bellicoso, C. D., Tsounis, V., Fankhauser, P., Diethelm, R., Bachmann, S., Bloesch, M., Kolvenbach, H., Bjelonic, M., Isler, L., & Meyer, K. (2017). ANYmal - toward legged robots for harsh environments. Advanced Robotics. https://doi.org/10.1080/01691864.2017.1378591

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.