Zobrazeno 1 - 10
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pro vyhledávání: '"Tapia, Lydia"'
Autor:
Tapia, Lydia
At first glance, robots and proteins have little in common. Robots are commonly thought of as tools that perform tasks such as vacuuming the floor, while proteins play essential roles in many biochemical processes. However, the functionality of both
Externí odkaz:
http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7313
Coordinated defensive escorts can aid a navigating payload by positioning themselves in order to maintain the safety of the payload from obstacles. In this paper, we present a novel, end-to-end solution for coordinating an escort team for protecting
Externí odkaz:
http://arxiv.org/abs/1910.04537
Publikováno v:
Robotics and Automation Letters 2019
This paper addresses two challenges facing sampling-based kinodynamic motion planning: a way to identify good candidate states for local transitions and the subsequent computationally intractable steering between these candidate states. Through the c
Externí odkaz:
http://arxiv.org/abs/1907.04799
Robot motion planning often requires finding trajectories that balance different user intents, or preferences. One of these preferences is usually arrival at the goal, while another might be obstacle avoidance. Here, we formalize these, and similar,
Externí odkaz:
http://arxiv.org/abs/1811.12651
Robots and autonomous agents often complete goal-based tasks with limited resources, relying on imperfect models and sensor measurements. In particular, reinforcement learning (RL) and feedback control can be used to help a robot achieve a goal. Taki
Externí odkaz:
http://arxiv.org/abs/1809.09261
Swept Volume (SV), the volume displaced by an object when it is moving along a trajectory, is considered a useful metric for motion planning. First, SV has been used to identify collisions along a trajectory, because it directly measures the amount o
Externí odkaz:
http://arxiv.org/abs/1805.11597
Autor:
Faust, Aleksandra, Ramirez, Oscar, Fiser, Marek, Oslund, Kenneth, Francis, Anthony, Davidson, James, Tapia, Lydia
Publikováno v:
IEEE International Conference on Robotics and Automation (ICRA), 2018
We present PRM-RL, a hierarchical method for long-range navigation task completion that combines sampling based path planning with reinforcement learning (RL). The RL agents learn short-range, point-to-point navigation policies that capture robot dyn
Externí odkaz:
http://arxiv.org/abs/1710.03937
Publikováno v:
In Artificial Intelligence June 2017 247:381-398
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Akademický článek
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