Zobrazeno 1 - 10
of 75
pro vyhledávání: '"Shkurti, Florian"'
Autor:
Singh, Ritvik, Liu, Jingzhou, Van Wyk, Karl, Chao, Yu-Wei, Lafleche, Jean-Francois, Shkurti, Florian, Ratliff, Nathan, Handa, Ankur
Vision-based object detectors are a crucial basis for robotics applications as they provide valuable information about object localisation in the environment. These need to ensure high reliability in different lighting conditions, occlusions, and vis
Externí odkaz:
http://arxiv.org/abs/2410.21153
Autor:
Huang, Jinbang, Tao, Allen, Marco, Rozilyn, Bogdanovic, Miroslav, Kelly, Jonathan, Shkurti, Florian
Task and motion planning (TAMP) frameworks address long and complex planning problems by integrating high-level task planners with low-level motion planners. However, existing TAMP methods rely heavily on the manual design of planning domains that sp
Externí odkaz:
http://arxiv.org/abs/2410.16445
Recent advancements in learned 3D representations have enabled significant progress in solving complex robotic manipulation tasks, particularly for rigid-body objects. However, manipulating granular materials such as beans, nuts, and rice, remains ch
Externí odkaz:
http://arxiv.org/abs/2410.09740
Spurious correlations in a reward model's training data can prevent Reinforcement Learning from Human Feedback (RLHF) from identifying the desired goal and induce unwanted behaviors. This paper shows that offline RLHF is susceptible to reward confusi
Externí odkaz:
http://arxiv.org/abs/2407.16025
Publikováno v:
Published 22 October 2024 (Early Access)
Robots need to predict and react to human motions to navigate through a crowd without collisions. Many existing methods decouple prediction from planning, which does not account for the interaction between robot and human motions and can lead to the
Externí odkaz:
http://arxiv.org/abs/2310.10982
Autor:
Lee, Yewon, Li, Andrew Z., Huang, Philip, Heiden, Eric, Jatavallabhula, Krishna Murthy, Damken, Fabian, Smith, Kevin, Nowrouzezahrai, Derek, Ramos, Fabio, Shkurti, Florian
Planning for sequential robotics tasks often requires integrated symbolic and geometric reasoning. TAMP algorithms typically solve these problems by performing a tree search over high-level task sequences while checking for kinematic and dynamic feas
Externí odkaz:
http://arxiv.org/abs/2310.01775
Autor:
Gu, Qiao, Kuwajerwala, Alihusein, Morin, Sacha, Jatavallabhula, Krishna Murthy, Sen, Bipasha, Agarwal, Aditya, Rivera, Corban, Paul, William, Ellis, Kirsty, Chellappa, Rama, Gan, Chuang, de Melo, Celso Miguel, Tenenbaum, Joshua B., Torralba, Antonio, Shkurti, Florian, Paull, Liam
For robots to perform a wide variety of tasks, they require a 3D representation of the world that is semantically rich, yet compact and efficient for task-driven perception and planning. Recent approaches have attempted to leverage features from larg
Externí odkaz:
http://arxiv.org/abs/2309.16650
Autor:
Abeysirigoonawardena, Yasasa, Xie, Kevin, Chen, Chuhan, Hosseini, Salar, Chen, Ruiting, Wang, Ruiqi, Shkurti, Florian
Self-driving software pipelines include components that are learned from a significant number of training examples, yet it remains challenging to evaluate the overall system's safety and generalization performance. Together with scaling up the real-w
Externí odkaz:
http://arxiv.org/abs/2309.15770
We introduce a multi-sensor navigation system for autonomous surface vessels (ASV) intended for water-quality monitoring in freshwater lakes. Our mission planner uses satellite imagery as a prior map, formulating offline a mission-level policy for gl
Externí odkaz:
http://arxiv.org/abs/2309.14657
In this paper we investigate the effect of the unpredictability of surrounding cars on an ego-car performing a driving maneuver. We use Maximum Entropy Inverse Reinforcement Learning to model reward functions for an ego-car conducting a lane change i
Externí odkaz:
http://arxiv.org/abs/2307.15287