[1]
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Paz, D., Xiang, H., Liang, A., and Christensen, H. I.
TridentNetV2: Lightweight graphical global plan representations for
dynamic trajectory generation.
In Intl Conf of Robotics and Automation (ICRA)
(Philadelphia, PA, May 2022), IEEE.
[ bib ]
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[2]
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Asfour, T., Yoshida, E., Park, J., Christensen, H., and Khatib, O., Eds.
Robotics Research - The 19th International Symposium ISRR,
vol. 20 of SPAR.
Springer Verlag, Berlin, February 2022.
[ bib ]
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[3]
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Paz, D., Zhang, H., and Christensen, H. I.
TridentNet: A conditional generative model for dynamic trajectory
generation.
In Intelligent Autonomous Systems-16 (Singapore, June 2021).
(Best paper).
[ bib ]
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[4]
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Han, Y., Liu, Y., Paz, D., and Christensen, H. I.
Auto-calibration method using stop signs for urban autonomous driving
applications.
In International Conference on Robotics and Automation (Xian,
May 2021), IEEE.
[ bib ]
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[5]
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Vuong, Q., Qin, Y., Guo, R., Wang, X., Su, H., and Christensen, H.
Single rgb-d camera teleoperation for general robotic manipulation,
2021.
[ bib |
arXiv ]
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[6]
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Nieto-Granda, C., Wang, S., Rogers III, J. G., and Christensen, H. I.
Distributed heterogeneous multi-robot source seeking using
information based sampling with visual recognition.
In 17th International Symposium on Experimental Robotics.
(Malta, Nov 2021), SPAR, Springer Verlag.
[ bib ]
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[7]
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Parashar, P., Naik, A., Hu, J., and Christensen, H. I.
A hierarchical model to enable plan reuse and repair in assembly
domains.
In IEEE Conference on Automation Science and Engineering
(Lyon, France, Aug 2021), pp. 387--394.
[ bib |
DOI ]
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[8]
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Langley, A., Dhiman, V., and Christensen, H. I.
Heterogenous multi-robot adversarial patrolling using poly-matrix
games.
In 7th International Conference on Robotics and Artificial
Intelligence (ICRAI) (Guangzhou, China, Nov 2021).
[ bib ]
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[9]
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Vincze, M., Patten, T., Christensen, H. I., Nalpantidos, L., and Liu, M.,
Eds.
Computer Vision Systems.
No. 12899 in LNCS. Springer Verlag, Vienna, Austria, Sep 2021.
[ bib ]
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[10]
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Christensen, H., Amato, N., Yanco, H., Mataric, M., Choset, H., Drobnis,
A., Goldberg, K., Grizzle, J., Hager, G., Hollerbach, J., Hutchinson, S.,
Krovi, V., Lee, D., Smart, B., Trinkle, J., and Sukhatme, G.
A roadmap for us robotics - from internet to robotics 2020 edition.
Foundations and Trends in Robotics 8, 4 (2021), 307--424.
[ bib |
DOI |
http ]
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[11]
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Christensen, H., Paz, D., Zhang, H., Meyer, D., Xiang, H., Han, Y., Liu,
Y., Liang, A., Zhong, Z., and Tang, S.
Autonomous vehicles for micro-mobility.
Springer - Autonomous and Intelligent Systems (Nov 2021).
[ bib ]
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[12]
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Qui, Y. C., Pal, A., and Christensen, H. I.
Target driven visual navigation exploiting object relationships.
In The Conference on Robotic Learning (Boston, MA, November
2020).
[ bib ]
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[13]
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H. I. Christensen (Ed.).
From internet to robotics - a US national robotics roadmap - 4th
edition.
Tech. rep., Computing Community Consortium & U.C. San Diego,
Washington, DC, September 2020.
[ bib |
.pdf ]
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[14]
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Pal, A., Mondal, S., and Christensen, H. I.
“looking at the right stuff” - guided semantic-gaze for autonomous
driving.
In Computer Vision and Pattern Recognition (CVPR) (Seattle,
WA, June 2020), IEEE/PAMI.
[ bib ]
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[15]
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Yang, G.-Z., Nelson, B., Murphy, R., Christensen, H., Collins, S., Dario,
P., Goldberg, K., Ikuta, K., Jacobstein, N., Kragic, D., Taylor, R., and
McNutt, M.
Combating covid-19 -- the role of robotics in managing public health
and infectious diseases.
Science Robotics (March 2020).
(Editorial).
[ bib ]
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[16]
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Paz, D., Zhang, H., Li, Q., Xiang, H., and Christensen, H. I.
Probabilistic semantic mapping for urban autonomous driving
applications.
In International Conference on Intelligent Robots and Systems
(IROS) (Las Vegas, NV, Oct 2020), IEEE/RSJ.
[ bib ]
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[17]
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Paz, D., Lai, P.-J., Chan, N., Jianf, Y., and Christensen, H. I.
Autonomous vehicle benchmarking using unbiased metrics.
In International Conference on Intelligent Robots and Systems
(IROS) (Las Vegas, NV, Oct 2020), IEEE/RSJ.
[ bib ]
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[18]
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Dieber, B., White, R., Taurer, S., Breiling, B., Caiazza, G., Christensen,
H., and Cortesi, A.
Penetration testing ros.
In Robot Operating System (ROS). Springer, 2020, pp. 183--225.
[ bib ]
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[19]
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D'Ambrosia, C., Christensen, H., and Arnoff-Spenncer, E.
Ruling In and Ruling Out COVID-19: Computing SARS-CoV-2
infection risk from symptoms, imaging and test data.
Journal of Medical Internet Research (JMIR) (Nov 2020).
[ bib ]
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[20]
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Paz, D., Lai, P.-J., Harish, S., Zhang, H., Chan, N., Hu, C., Binnani, S.,
and Christensen, H.
Lessons learned from deploying autonomous vehicles at UC San Diego.
In Field and Service Robotics (Tokyo, JP, August 2019).
[ bib |
http ]
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[21]
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Wang, S., Liu, X., Zhao, J., and Christensen, H.
Robotic reliability engineering: A story of long-term tritonbot
development.
In Field and Service Robotics (Tokyo, JP, August 2019).
[ bib ]
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[22]
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White, R., Caiazza, G., Jiang, C., Ou, X., Yang, Z., Cortesi, A., and
Christensen, H.
Network reconnaissance and vulnerability excavation of secure dds
systems.
In Workshop on Software Security for Internet of Things
(Stockholm, Sweden, June 2019), IEEE and Euro S&P.
[ bib ]
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[23]
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Fung, N., Rogers III, J. G., Nieto-Granda, C., Christensen, H. I.,
Kemna, S., and Sukhatme, G.
Coordinating multi-robot systems through environment partitioning for
adaptive informative sampling.
In Intl. Conf. Robotics and Automation (Montreal, May 2019),
IEEE.
[ bib ]
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[24]
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Wang, S., Liu, X., Zhao, J., and Christensen, H. I.
Rorg: Service robot software management with linux containers.
In Intl. Conf. Robotics and Automation (Montreal, May 2019),
IEEE.
[ bib ]
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[25]
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Li, S., and Christensen, H. I.
WaveToFly: wavetofly: Control a uav using body gestures.
In Intl. Conf. Robotics and Automation (Montreal, May 2019),
IEEE.
[ bib ]
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[26]
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Parashar, P., Sanneman, L. M., Christensen, H. I., and Shah, J. A.
Enabling efficient team cooperation by understanding modes of
human-robot interactions.
In Intl. Conf. Robotics and Automation (Montreal, May 2019).
[ bib ]
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[27]
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Pal, A., Nieto, C., and Christensen, H. I.
DEDUCE: Diverse scEne Detection methods in Unseen
Challenging Environments.
In International Conference on Intelligent Robots and Systems
(Macau, Oct 2019), IEEE/RSJ.
[ bib ]
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[28]
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Parashar, P., Sanneman, L., Christensen, H. I., and Shah, J. A.
A taxonomy for characterizing modes of interactions in goal-driven,
human-robot teams.
In International Conference on Intelligent Robots and Systems
(Macau, Oct 2019), IEEE/RSJ.
[ bib ]
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[29]
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White, R., Caiazza, G., Cortesi, A., Cho, Y. I., and Christensen, H. I.
Black block recorder: Immutable black box logging for robots via
blockchain.
In International Conference on Intelligent Robots and Systems
(Macau, Oct 2019), IEEE/RSJ, pp. 1--8.
[ bib ]
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[30]
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Yang, G.-Z., Full, R. J., Jacobstein, N., Fischer, P., Bellingham, J.,
Choset, H., Christensen, H., Dario, P., Nelson, B. J., and Taylor, R.
Ten technologies of the year.
Science Robotics 4 (2019).
[ bib ]
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[31]
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Wang, S., and Christensen, H. I.
Tritonbot: First lessons learned from deployment of a long-term
autonomy tour guide robot.
In RoMan (Nanjing, China, August 2018), IEEE/RSJ.
[ bib ]
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[32]
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Sawhney, R., Li, F., Christensen, H. I., and Isbell, C. L.
Purely geometric scene association and retrieval - a case for
macro-scale 3d geometry.
In Intl. Conf. on Robotics and Automation (Brisbane, May
2018), IEEE.
[ bib ]
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[33]
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Huamán Quispe, A., Ben Amor, H., and Christensen, H. I.
A Taxonomy of Benchmark Tasks for Robot Manipulation.
Springer International Publishing, Cham, 2018, pp. 405--421.
[ bib |
DOI |
http ]
This paper presents a taxonomy of benchmark
manipulation tasks for service robots. Our
contributions are threefold: (1) A review of
relevant literature regarding manipulation tests in
the robotics domain and related fields, such as
physical therapy, assistive technologies and
prosthetics. (2) Guidelines to design useful testing
protocols to evaluate manipulation performance. (3)
A proposed general taxonomy of benchmark
manipulation tasks and sample tests per each class.
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[34]
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White, R., Caiazza, G., Christensen, H. I., and Cortesi, A.
Procedurally provisioned access control for robotic systems.
In RosCon (Madrid, Oct 2018).
[ bib ]
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[35]
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Nieto, C., Rogers, J., Fung, N., Kemna, S., Christensen, H. I., and
Sukhatme, G.
On-line Coordination Task for Multi-robot Systemsusing
Adaptive Informative Sampling.
In Intl. Symp. Exp. Robotics (Buenos Aires, Nov 2018), STAR,
IFRR, Springer.
[ bib ]
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[36]
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Berman, F., Rutenbar, R. A., Hailpern, B., Christensen, H., Davidson, S.,
Estrin, D., Franklin, M., Martonosi, M., Raghavan, P., Stodden, V., and
Szalay, A. S.
Realizing the potential of data science.
Communications of the ACM 61, 4 (Apr 2018), 67--72.
[ bib |
http ]
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[37]
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Cosgun, A., and Christensen, H. I.
Context aware robot navigation using interactively built semantic
maps.
Paladyn. Journal of Behavioral Robotics 9, 1 (2018), 254--276.
[ bib ]
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[38]
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Price, A., Balakirsky, S., and Christensen, H.
Robust grasp preimages under unknown mass and friction distributions.
Integrated Computer Aided Engineering 25, 2 (Mar 2018),
99--110.
[ bib ]
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[39]
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Parashar, P., Goel, A., Sheneman, B., and Christensen, H. I.
Towards lifelong adaptive agents: Using meta- reasoning for combining
task planning and situated learning.
The Knowledge Engineering Review 33, 18 (Oct 2018).
[ bib ]
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[40]
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Brahmbhatt, S., Christensen, H., and Hays, J.
Stuffnet: Using “stuff” to improve object detection.
In IEEE Winter Conference on Applications of Computer Vision
(WACV) (2017).
[ bib ]
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[41]
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Shih, B., Drotman, D., Christianson, C., Huo, Z., White, R., Christensen,
H. I., and Tolley, M. T.
Custom soft robotic gripper sensor skins for haptic object
visualization.
In Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ
International Conference on (2017), IEEE, pp. 494--501.
[ bib ]
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[42]
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Christensen, H. I., Khan, A., Pokutta, S., and Tetali, P.
Approximation and online algorithms for multidimensional bin packing:
A survey.
Computer Science Review (2017), --.
[ bib |
DOI |
www: ]
Keywords: Approximation algorithms
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[43]
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Choudhary, S., Carlone, L., Nieto, C., Rogers, J., Christensen, H. I., and
Dellaert, F.
Distributed mapping with privacy and communication constraints:
Lightweight algorithms and object-based models.
The International Journal of Robotics Research 36, 12 (2017),
1286--1311.
[ bib |
DOI |
arXiv |
www: ]
We consider the following problem: a team of robots
is deployed in an unknown environment and it has to
collaboratively build a map of the area without a
reliable infrastructure for communication. The
backbone for modern mapping techniques is pose graph
optimization, which estimates the trajectory of the
robots, from which the map can be easily built. The
first contribution of this paper is a set of
distributed algorithms for pose graph optimization:
rather than sending all sensor data to a remote
sensor fusion server, the robots exchange very
partial and noisy information to reach an agreement
on the pose graph configuration. Our approach can be
considered as a distributed implementation of a
two-stage approach that already exists, where we use
the Successive Over-Relaxation and the Jacobi
Over-Relaxation as workhorses to split the
computation among the robots. We also provide
conditions under which the proposed distributed
protocols converge to the solution of the
centralized two-stage approach. As a second
contribution, we extend the proposed distributed
algorithms to work with the object-based map
models. The use of object-based models avoids the
exchange of raw sensor measurements (e.g. point
clouds or RGB-D data) further reducing the
communication burden. Our third contribution is an
extensive experimental evaluation of the proposed
techniques, including tests in realistic Gazebo
simulations and field experiments in a military test
facility. Abundant experimental evidence suggests
that one of the proposed algorithms (the Distributed
Gauss–Seidel method) has excellent performance. The
Distributed Gauss–Seidel method requires minimal
information exchange, has an anytime flavor, scales
well to large teams (we demonstrate mapping with a
team of 50 robots), is robust to noise, and is easy
to implement. Our field tests show that the combined
use of our distributed algorithms and object-based
models reduces the communication requirements by
several orders of magnitude and enables distributed
mapping with large teams of robots in real-world
problems. The source code is available for download
at https://cognitiverobotics.github.io/distributed-mapper/
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[44]
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Christensen, H. I., Okamura, A., Kumar, V., Hager, G., and Choset, H.
Next generation robotics.
Tech. rep., Computing Community Consortium, Washington, DC, June
2016.
[ bib |
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