Search Results - "Agogino, Adrian K."

Rolling Locomotion of Cable-Driven Soft Spherical Tensegrity Robots by Kim, Kyunam, Agogino, Adrian K, Agogino, Alice M

“…Soft spherical tensegrity robots are novel steerable mobile robotic platforms that are compliant, lightweight, and robust. The geometry of these robots is…”
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Inverse Statics Optimization for Compound Tensegrity Robots by Sabelhaus, Andrew P., Li, Albert H., Sover, Kimberly A., Madden, Jacob R., Barkan, Andrew R., Agogino, Adrian K., Agogino, Alice M.

“…Robots built from cable-driven tensegrity ('tension-integrity') structures have many of the advantages of soft robots, such as flexibility and robustness, yet…”
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Robust learning of tensegrity robot control for locomotion through form-finding by Kyunam Kim, Agogino, Adrian K., Toghyan, Aliakbar, Moon, Deaho, Taneja, Laqshya, Agogino, Alice M.

“…Robots based on tensegrity structures have the potential to be robust, efficient and adaptable. While traditionally being difficult to control, recent control…”
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A multiagent approach to managing air traffic flow by Agogino, Adrian K., Tumer, Kagan

“…Intelligent air traffic flow management is one of the fundamental challenges facing the Federal Aviation Administration (FAA) today. FAA estimates put weather,…”
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Model-Predictive Control With Inverse Statics Optimization for Tensegrity Spine Robots by Sabelhaus, Andrew P., Zhao, Huajing, Zhu, Edward L., Agogino, Adrian K., Agogino, Alice M.

“…Robots with flexible spines based on tensegrity structures have potential advantages over traditional designs with rigid torsos. However, these robots can be…”
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Ensemble clustering with voting active clusters by Tumer, Kagan, Agogino, Adrian K.

“…Clustering is an integral part of pattern recognition problems and is connected to both the data reduction and the data understanding steps. Combining multiple…”
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Simulation of the introduction of new technologies in air traffic management by Yliniemi, Logan, Agogino, Adrian K., Tumer, Kagan

“…Accurate simulation of the effects of integrating new technologies into a complex system is critical to the modernisation of large infrastructure problems…”
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Rapid prototyping design and control of tensegrity soft robot for locomotion by Kyunam Kim, Agogino, Adrian K., Deaho Moon, Taneja, Laqshya, Toghyan, Aliakbar, Dehghani, Borna, SunSpiral, Vytas, Agogino, Alice M.

“…Co-robots that can effectively move with and operate alongside humans in a variety of conditions could revolutionize the utility of robots for a wide range of…”
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Analyzing and visualizing multiagent rewards in dynamic and stochastic domains by Agogino, Adrian K., Tumer, Kagan

“…The ability to analyze the effectiveness of agent reward structures is critical to the successful design of multiagent learning algorithms. Though final system…”
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Hopping and rolling locomotion with spherical tensegrity robots by Kyunam Kim, Lee-Huang Chen, Cera, Brian, Daly, Mallory, Zhu, Edward, Despois, Julien, Agogino, Adrian K., SunSpiral, Vytas, Agogino, Alice M.

“…This work presents a 10 kg tensegrity ball probe that can quickly and precisely deliver a 1 kg payload over a 1 km distance on the Moon by combining…”
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Unifying Temporal and Structural Credit Assignment Problems by Agogino, Adrian K., Tumer, Kagan

“…Single-agent reinforcement learners in time-extended domains and multi-agent systems share a common difficulty known as the credit assignment problem…”
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Inclined surface locomotion strategies for spherical tensegrity robots by Lee-Huang Chen, Cera, Brian, Zhu, Edward L., Edmunds, Riley, Rice, Franklin, Bronars, Antonia, Tang, Ellande, Malekshahi, Saunon R., Romero, Osvaldo, Agogino, Adrian K., Agogino, Alice M.

“…This paper presents a new teleoperated spherical tensegrity robot capable of performing locomotion on steep inclined surfaces. With a novel control scheme…”
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Time-Extended Policies in Multi-Agent Reinforcement Learning by Tumer, Kagan, Agogino, Adrian K.

“…Many algorithms such as Q-learning successfully address reinforcement learning in single-agent multi-time-step problems. In addition there are methods that…”
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Overcoming communication restrictions in collectives by Tumer, K., Agogino, A.K.

“…The performance of distributed systems generally depend on the actions and interactions of a large number of independent components (e.g., agents, neurons)…”
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Handling Communication Restrictions and Team Formation in Congestion Games by Agogino, Adrian K., Tumer, Kagan

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Model-Predictive Control with Inverse Statics Optimization for Tensegrity Spine Robots by Sabelhaus, Andrew P, Zhao, Huajing, Zhu, Edward L, Agogino, Adrian K, Agogino, Alice M

“…Robots with flexible spines based on tensegrity structures have potential advantages over traditional designs with rigid torsos. However, these robots can be…”
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Inverse Statics Optimization for Compound Tensegrity Robots by Sabelhaus, Andrew P, Li, Albert H, Sover, Kimberly A, Madden, Jacob, Barkan, Andrew, Agogino, Adrian K, Agogino, Alice M

“…Robots built from cable-driven tensegrity (`tension-integrity') structures have many of the advantages of soft robots, such as flexibility and robustness,…”
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Design, Simulation, and Testing of a Flexible Actuated Spine for Quadruped Robots by Sabelhaus, Andrew P, van Vuuren, Lara Janse, Joshi, Ankita, Zhu, Edward, Garnier, Hunter J, Sover, Kimberly A, Navarro, Jesus, Agogino, Adrian K, Agogino, Alice M

“…Walking quadruped robots face challenges in positioning their feet and lifting their legs during gait cycles over uneven terrain. The robot Laika is under…”
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Inclined Surface Locomotion Strategies for Spherical Tensegrity Robots by Chen, Lee-Huang, Cera, Brian, Zhu, Edward L, Edmunds, Riley, Rice, Franklin, Bronars, Antonia, Tang, Ellande, Malekshahi, Saunon R, Romero, Osvaldo, Agogino, Adrian K, Agogino, Alice M

“…This paper presents a new teleoperated spherical tensegrity robot capable of performing locomotion on steep inclined surfaces. With a novel control scheme…”
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CLEAN Rewards to Improve Coordination by Removing Exploratory Action Noise by Holmesparker, Chris, Taylor, Mathew E., Agogino, Adrian K., Tumer, Kagan

“…Coordinating the joint-actions of agents in cooperative multiagent systems is a difficult problem in many real world domains. Learning in such multiagent…”
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