Cikkek nyilvánosan hozzáférhető megbízással - Russ TedrakeTovábbi információ
Valahol hozzáférhető: 70
Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot
S Kuindersma, R Deits, M Fallon, A Valenzuela, H Dai, F Permenter, ...
Autonomous robots 40, 429-455, 2016
Megbízások: US National Science Foundation
Diffusion policy: Visuomotor policy learning via action diffusion
C Chi, Z Xu, S Feng, E Cousineau, Y Du, B Burchfiel, R Tedrake, S Song
The International Journal of Robotics Research, 02783649241273668, 2023
Megbízások: US National Science Foundation
Funnel libraries for real-time robust feedback motion planning
A Majumdar, R Tedrake
The International Journal of Robotics Research 36 (8), 947-982, 2017
Megbízások: US Department of Defense
Dynamics and trajectory optimization for a soft spatial fluidic elastomer manipulator
AD Marchese, R Tedrake, D Rus
The International Journal of Robotics Research 35 (8), 1000-1019, 2016
Megbízások: US National Science Foundation
kpam: Keypoint affordances for category-level robotic manipulation
L Manuelli, W Gao, P Florence, R Tedrake
The International Symposium of Robotics Research, 132-157, 2019
Megbízások: US National Science Foundation
Scalable end-to-end autonomous vehicle testing via rare-event simulation
M O'Kelly, A Sinha, H Namkoong, R Tedrake, JC Duchi
Advances in neural information processing systems 31, 2018
Megbízások: US National Science Foundation
Optimization and stabilization of trajectories for constrained dynamical systems
M Posa, S Kuindersma, R Tedrake
2016 IEEE International Conference on Robotics and Automation (ICRA), 1366-1373, 2016
Megbízások: US National Science Foundation
Self-supervised correspondence in visuomotor policy learning
P Florence, L Manuelli, R Tedrake
IEEE Robotics and Automation Letters 5 (2), 492-499, 2019
Megbízások: US National Science Foundation
An architecture for online affordance‐based perception and whole‐body planning
M Fallon, S Kuindersma, S Karumanchi, M Antone, T Schneider, H Dai, ...
Journal of Field Robotics 32 (2), 229-254, 2015
Megbízások: US Department of Energy
Propagation networks for model-based control under partial observation
Y Li, J Wu, JY Zhu, JB Tenenbaum, A Torralba, R Tedrake
2019 International Conference on Robotics and Automation (ICRA), 1205-1211, 2019
Megbízások: US National Science Foundation, US Department of Defense, US National …
Filterreg: Robust and efficient probabilistic point-set registration using gaussian filter and twist parameterization
W Gao, R Tedrake
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019
Megbízások: US National Science Foundation
Fast trajectory optimization for agile quadrotor maneuvers with a cable-suspended payload
P Foehn, D Falanga, N Kuppuswamy, R Tedrake, D Scaramuzza
Robotics: Science and Systems Foundation, 2017
Megbízások: Swiss National Science Foundation, US Department of Defense, US National …
Label fusion: A pipeline for generating ground truth labels for real rgbd data of cluttered scenes
P Marion, PR Florence, L Manuelli, R Tedrake
2018 IEEE International Conference on Robotics and Automation (ICRA), 3235-3242, 2018
Megbízások: US National Science Foundation, US Department of Defense
Connecting touch and vision via cross-modal prediction
Y Li, JY Zhu, R Tedrake, A Torralba
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
Megbízások: US National Aeronautics and Space Administration
Motion planning around obstacles with convex optimization
T Marcucci, M Petersen, D von Wrangel, R Tedrake
Science robotics 8 (84), eadf7843, 2023
Megbízások: US National Science Foundation, US Department of Defense
Localizing external contact using proprioceptive sensors: The contact particle filter
L Manuelli, R Tedrake
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2016
Megbízások: US National Science Foundation
High‐speed autonomous obstacle avoidance with pushbroom stereo
AJ Barry, PR Florence, R Tedrake
Journal of Field Robotics 35 (1), 52-68, 2018
Megbízások: US Department of Defense
Do differentiable simulators give better policy gradients?
HJ Suh, M Simchowitz, K Zhang, R Tedrake
International Conference on Machine Learning, 20668-20696, 2022
Megbízások: US National Science Foundation, US Department of Defense
Linear encodings for polytope containment problems
S Sadraddini, R Tedrake
2019 IEEE 58th conference on decision and control (CDC), 4367-4372, 2019
Megbízások: US Department of Defense
Tracking objects with point clouds from vision and touch
G Izatt, G Mirano, E Adelson, R Tedrake
2017 IEEE International Conference on Robotics and Automation (ICRA), 4000-4007, 2017
Megbízások: US National Science Foundation
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