David Held
David Held
Assistant Professor in the Robotics Institute, Carnegie Mellon University
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Zitiert von
Zitiert von
Towards fully autonomous driving: Systems and algorithms
J Levinson, J Askeland, J Becker, J Dolson, D Held, S Kammel, JZ Kolter, ...
2011 IEEE intelligent vehicles symposium (IV), 163-168, 2011
Learning to track at 100 fps with deep regression networks
D Held, S Thrun, S Savarese
Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016
Constrained policy optimization
J Achiam, D Held, A Tamar, P Abbeel
International conference on machine learning, 22-31, 2017
Pcn: Point completion network
W Yuan, T Khot, D Held, C Mertz, M Hebert
2018 international conference on 3D vision (3DV), 728-737, 2018
Automatic goal generation for reinforcement learning agents
C Florensa, D Held, X Geng, P Abbeel
International conference on machine learning, 1515-1528, 2018
Reverse curriculum generation for reinforcement learning
C Florensa, D Held, M Wulfmeier, M Zhang, P Abbeel
Conference on robot learning, 482-495, 2017
3d multi-object tracking: A baseline and new evaluation metrics
X Weng, J Wang, D Held, K Kitani
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020
Enabling robots to communicate their objectives
SH Huang, D Held, P Abbeel, AD Dragan
Autonomous Robots 43, 309-326, 2019
Softgym: Benchmarking deep reinforcement learning for deformable object manipulation
X Lin, Y Wang, J Olkin, D Held
Conference on Robot Learning, 432-448, 2021
Just go with the flow: Self-supervised scene flow estimation
H Mittal, B Okorn, D Held
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020
What you see is what you get: Exploiting visibility for 3d object detection
P Hu, J Ziglar, D Held, D Ramanan
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
Combining 3D Shape, Color, and Motion for Robust Anytime Tracking
D Held, J Levinson, S Thrun, S Savarese
Robotics: Science and Systems, 2014
Plas: Latent action space for offline reinforcement learning
W Zhou, S Bajracharya, D Held
Conference on Robot Learning, 1719-1735, 2021
Adaptive auxiliary task weighting for reinforcement learning
X Lin, H Baweja, G Kantor, D Held
Advances in neural information processing systems 32, 2019
Precision Tracking with Sparse 3D and Dense Color 2D Data
D Held, J Levinson, S Thrun
Robust single-view instance recognition
D Held, S Thrun, S Savarese
2016 IEEE International Conference on Robotics and Automation (ICRA), 2152-2159, 2016
MVWT-II: The second generation caltech multi-vehicle wireless testbed
Z Jin, S Waydo, EB Wildanger, M Lammers, H Scholze, P Foley, D Held, ...
Proceedings of the 2004 American control conference 6, 5321-5326, 2004
Learning visible connectivity dynamics for cloth smoothing
X Lin, Y Wang, Z Huang, D Held
Conference on Robot Learning, 256-266, 2022
Ab3dmot: A baseline for 3d multi-object tracking and new evaluation metrics
X Weng, J Wang, D Held, K Kitani
arXiv preprint arXiv:2008.08063, 2020
Robust real-time tracking combining 3D shape, color, and motion
D Held, J Levinson, S Thrun, S Savarese
The International Journal of Robotics Research 35 (1-3), 30-49, 2016
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