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Skip to Search Results- 2Parker, Christopher A. C.
- 1Akturk, Sait
- 1Austin, James A.
- 1Carlos Manuel Martínez
- 1Funabashi, Martha
- 1Hajebi, Kiana
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Fall 2012
Crane lift path planning is time-consuming, prone to errors, and requires the practitioners to have exceptional visualization abilities, in particular, as the construction site is congested and dynamically changing. This research presents a methodology based on robotics motion planning to...
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Spring 2023
Choosing an appropriate action representation is an integral part of solving robotic manipulation problems. Published approaches include latent action models, which train context-conditioned neural networks to map lowdimensional latent actions to high-dimensional actuation commands. Such models...
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Spring 2023
Wheelchair-mounted robotic manipulators have the potential to help the elderly and individuals living with disabilities carry out their activities of daily living independently. While robotics researchers focus on assistive tasks from the perspective of various control schemes and motion types, ...
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Back2Future-SIM: Creating Real-Time Interactable Immersive Virtual World For Robot Teleoperation
DownloadSpring 2024
In the context of human-robot interaction (HRI), robot autonomy addresses how environmental input influences a robot’s actions, spanning a spectrum from full human control to independent robot motion. The quality of HRI relies on information exchange, evaluated through factors like interaction...
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Collective decision-making in decentralized multiple-robot systems: a biologically inspired approach to making up all of your minds
DownloadFall 2009
Decision-making is an important operation for any autonomous system. Robots in particular must observe their environment and compute appropriate responses. For solitary robots and centralized multiple-robot systems, decision-making is a relatively straightforward operation, since only a single...
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Decision Frequency Adaptation in Reinforcement Learning Using Continuous Options with Open-Loop Policies
DownloadFall 2023
In classic reinforcement learning(RL) for continuous control, agents make decisions at discrete and fixed time intervals. The duration between decisions becomes a crucial hyperparameter. Setting it too short may increase the problem’s difficulty by requiring the agent to make numerous decisions...
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Fall 2018
The adoption of powered myoelectric prostheses and their ability to improve quality of life for persons with amputations is hindered by the difficulty of controlling multiple degrees of freedom with a limited number of input signals. Different myoelectric control strategies have been developed to...
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Fall 2015
Understanding how an artificial agent may represent, acquire, update, and use large amounts of knowledge has long been an important research challenge in artificial intelligence. The quantity of knowledge, or knowing a lot, may be nicely thought of as making and updat- ing many predictions about...