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Alice M. Agogino

12 accepted papers

2021

Force-Sensing Tensegrity for Investigating Physical Human-Robot Interaction in Compliant Robotic Systems

Andrew R. Barkan, Akhil Padmanabha, Sala R. Tiemann, Albert Lee, Matthew P. Kanter, Yash S. Agarwal +1
ICRA 2021poster

Advancements in the domain of physical human-robot interaction (pHRI) have tremendously improved the ability of humans and robots to communicate, collaborate, and coexist. In particular, compliant robotic systems offer many characteristics that can be leveraged towards enabling physical interactions…

Cited by 9Source ↗Scholar ↗
2020

Inverse Statics Optimization for Compound Tensegrity Robots

Andrew P. Sabelhaus, Albert H. Li, Kimberly A. Sover, Jacob R. Madden, Andrew R. Barkan, Adrian K. Agogino +1
RA-L 2020

Robots built from cable-driven tensegrity (`tension-integrity') structures have many of the advantages of soft robots, such as flexibility and robustness, yet still obey simple statics and dynamics models. However, existing approaches cannot natively model tensegrity robots with arbitrary rigid bodi

Cited by 33Source ↗Scholar ↗
2019

Energy-Efficient Locomotion Strategies and Performance Benchmarks using Point Mass Tensegrity Dynamics

Brian M. Cera, Anthony A. Thompson, Alice M. Agogino
IROS 2019poster

This work introduces a novel 12-motor paired-cable actuation scheme to achieve rolling locomotion with a spherical tensegrity structure. Using a new point mass tensegrity dynamic formulation which we present, we utilize Model Predictive Control to generate optimal state-action trajectories for bench…

Cited by 6Source ↗Scholar ↗
2019

Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assembly

Jianlan Luo, Eugen Solowjow, Chengtao Wen, Juan Aparicio Ojea, Alice M. Agogino, Aviv Tamar +1
ICRA 2019poster

Precise robotic manipulation skills are desirable in many industrial settings, reinforcement learning (RL) methods hold the promise of acquiring these skills autonomously. In this paper, we explicitly consider incorporating operational space force/torque information into reinforcement learning; this…

Cited by 243Source ↗Scholar ↗
2018

Deep Reinforcement Learning for Robotic Assembly of Mixed Deformable and Rigid Objects

Jianlan Luo, Eugen Solowjow, Chengtao Wen, Juan Aparicio Ojea, Alice M. Agogino
IROS 2018poster

Reinforcement learning for assembly tasks can yield powerful robot control algorithms for applications that are challenging or even impossible for “conventional” feedback control methods. Insertion of a rigid peg into a deformable hole of smaller diameter is such a task. In this contribution we solv…

Cited by 113Source ↗Scholar ↗
2018

Multi-Cable Rolling Locomotion with Spherical Tensegrities Using Model Predictive Control and Deep Learning

Brian Cera, Alice M. Agogino
IROS 2018poster

This work presents a model-based approach for creating robust control policies for rolling locomotion with a spherical tensegrity topology. Utilizing the structured dynamics of Class-1 tensegrity systems, we turn to model predictive control (MPC) to generate optimal multi-cable actuation trajectorie…

Cited by 32Source ↗Scholar ↗
2018

Tensegrity Robot Locomotion Under Limited Sensory Inputs via Deep Reinforcement Learning

Jianlan Luo, Riley Edmunds, Franklin Rice, Alice M. Agogino
ICRA 2018poster

Tensegrity robots are composed of rigid rods connected by elastic cables, and their unique light-weight yet compliant structure makes them an appealing choice for space exploration. However, locomotion control for these robotic systems remains difficult due to their nonlinear dynamics and high-dimen…

Cited by 41Source ↗Scholar ↗
2017

Inclined surface locomotion strategies for spherical tensegrity robots

Lee-Huang Chen, Brian Cera, Edward L. Zhu, Riley Edmunds, Franklin Rice, Antonia Bronars +5
IROS 2017poster

This paper presents a new teleoperated spherical tensegrity robot capable of performing locomotion on steep inclined surfaces. With a novel control scheme centered around the simultaneous actuation of multiple cables, the robot demonstrates robust climbing on inclined surfaces in hardware experiment…

Cited by 47Source ↗Scholar ↗
2016

Hopping and rolling locomotion with spherical tensegrity robots

Kyunam Kim, Lee-Huang Chen, Brian Cera, Mallory Daly, Edward Zhu, Julien Despois +3
IROS 2016poster

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 cable-driven rolling and thruster-based hopping. Previous research has shown that cable-driven rolling is effective for precise positioning, even in ro…

Cited by 80Source ↗Scholar ↗
2015

Robust learning of tensegrity robot control for locomotion through form-finding

Kyunam Kim, Adrian K. Agogino, Aliakbar Toghyan, Deaho Moon, Laqshya Taneja, Alice M. Agogino
IROS 2015poster

Robots based on tensegrity structures have the potential to be robust, efficient and adaptable. While traditionally being difficult to control, recent control strategies for ball-shaped tensegrity robots have successfully enabled punctuated rolling, hill-climbing and obstacle climbing. These gains h…

Cited by 75Source ↗Scholar ↗
2015

System design and locomotion of SUPERball, an untethered tensegrity robot

Andrew P. Sabelhaus, Jonathan Bruce, Ken Caluwaerts, Pavlo Manovi, Roya Fallah Firoozi, Sarah Dobi +2
ICRA 2015poster

The Spherical Underactuated Planetary Exploration Robot ball (SUPERball) is an ongoing project within NASA Ames Research Center's Intelligent Robotics Group and the Dynamic Tensegrity Robotics Lab (DTRL). The current SUPERball is the first full prototype of this tensegrity robot platform, eventually…

Cited by 261Source ↗Scholar ↗