The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
WALK‐MAN: A High‐Performance Humanoid Platform for Realistic Environments
Open accessAbstract
In this work, we present WALK‐MAN, a humanoid platform that has been developed to operate in realistic unstructured environment, and demonstrate new skills including powerful manipulation, robust balanced locomotion, high‐strength capabilities, and physical sturdiness. To enable these capabilities, WALK‐MAN design and actuation are based on the most recent advancements of series elastic actuator drives with unique performance features that differentiate the robot from previous state‐of‐the‐art compliant actuated robots. Physical interaction performance is benefited by both active and passive adaptation, thanks to WALK‐MAN actuation that combines customized high‐performance modules with tuned torque/velocity curves and transmission elasticity for high‐speed adaptation response and motion reactions to disturbances. WALK‐MAN design also includes innovative design optimization features that consider the selection of kinematic structure and the placement of the actuators with the body structure to maximize the robot performance. Physical robustness is ensured with the integration of elastic transmission, proprioceptive sensing, and control. The WALK‐MAN hardware was designed and built in 11 months, and the prototype of the robot was ready four months before DARPA Robotics Challenge (DRC) Finals. The motion generation of WALK‐MAN is based on the unified motion‐generation framework of whole‐body locomotion and manipulation (termed loco‐manipulation). WALK‐MAN is able to execute simple loco‐manipulation behaviors synthesized by combining different primitives defining the behavior of the center of gravity, the motion of the hands, legs, and head, the body attitude and posture, and the constrained body parts such as joint limits and contacts. The motion‐generation framework including the specific motion modules and software architecture is discussed in detail. A rich perception system allows the robot to perceive and generate 3D representations of the environment as well as detect contacts and sense physical interaction force and moments. The operator station that pilots use to control the robot provides a rich pilot interface with different control modes and a number of teleoperated or semiautonomous command features. The capability of the robot and the performance of the individual motion control and perception modules were validated during the DRC in which the robot was able to demonstrate exceptional physical resilience and execute some of the tasks during the competition.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
Cited by
ANYmal - toward legged robots for harsh environments
Hutter, M., Gehring, C., Lauber, A., Gunther, F., Bellicoso, C. D., Tsounis, V., Fankhauser, P., Diethelm, R., Bachmann, S., Bloesch, M., et al. (2017) · Advanced Robotics · doi:10.1080/01691864.2017.1378591 · Cited by 341
AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection
Do, T.-T., Nguyen, A., & Reid, I. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8460902 · Cited by 305
Dynamic movement primitives in robotics: A tutorial survey
Saveriano, M., Abu-Dakka, F. J., Kramberger, A., & Peternel, L. (2023) · The International Journal of Robotics Research · doi:10.1177/02783649231201196 · Cited by 267
Introduction to the Use of Robotic Tools for Search and Rescue
Cubber, G. D., Doroftei, D., Rudin, K., Berns, K., Matos, A., Serrano, D., Sanchez, J., Govindaraj, S., Bedkowski, J., Roda, R., et al. (2017) · Search and Rescue Robotics - From Theory to Practice · doi:10.5772/intechopen.69489 · Cited by 40
Metadata
- Authors
- Tsagarakis, N. G., Caldwell, D. G., Negrello, F., Choi, W., Baccelliere, L., Loc, V. G., Noorden, J., Muratore, L., Margan, A., Cardellino, A., Natale, L., Mingo Hoffman, E., Dallali, H., Kashiri, N., Malzahn, J., Lee, J., Kryczka, P., Kanoulas, D., Garabini, M., Catalano, M., Ferrati, M., Varricchio, V., Pallottino, L., Pavan, C., Bicchi, A., Settimi, A., Rocchi, A., & Ajoudani, A.
- Venue
- Journal of Field Robotics
- Publisher
- Wiley
- Published
- 2017-06-28
- Volume
- 34
- Issue
- 7
- Pages
- 1225-1259
- ISSN
- 1556-4959 (ISSN-L)
- 1556-4967
- OpenAlex
- W2730083658
- Cited by
- 272
- References
- 1
- FWCI
- 16.53
- 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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: WALK‐MAN: A High‐Performance Humanoid Platform for Realistic Environments. https://app.cochat.ai/papers/10.1002/rob.21702
- The paper
- Tsagarakis, N. G., Caldwell, D. G., Negrello, F., Choi, W., Baccelliere, L., Loc, V. G., Noorden, J., Muratore, L., Margan, A., Cardellino, A., Natale, L., Mingo Hoffman, E., Dallali, H., Kashiri, N., Malzahn, J., Lee, J., Kryczka, P., Kanoulas, D., Garabini, M., . . . Ajoudani, A. (2017). WALK‐MAN: A High‐Performance Humanoid Platform for Realistic Environments. Journal of Field Robotics. https://doi.org/10.1002/rob.21702
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.