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In this work we present a framework that is capable of accurately representing soft robotic actuators in a multiphysics environment in real-time. We propose a constraint-based dynamics model of a 1-dimensional pneumatic soft actuator that…

Robotics · Computer Science 2019-04-08 Renato Gasoto , Miles Macklin , Xuan Liu , Yinan Sun , Kenny Erleben , Cagdas Onal , Jie Fu

Koopman-based learning methods can potentially be practical and powerful tools for dynamical robotic systems. However, common methods to construct Koopman representations seek to learn lifted linear models that cannot capture nonlinear…

Robotics · Computer Science 2021-05-18 Carl Folkestad , Joel W. Burdick

Soft robots have a myriad of potentials because of their intrinsically compliant bodies, enabling safe interactions with humans and adaptability to unpredictable environments. However, most of them have limited actuation speeds, require…

Bimanual handovers are crucial for transferring large, deformable or delicate objects. This paper proposes a framework for generating kinematically constrained human-like bimanual robot motions to ensure seamless and natural robot-to-human…

Today's heavy-duty mobile machines (HDMMs) face two transitions: from diesel-hydraulic actuation to clean electric systems driven by climate goals, and from human supervision toward greater autonomy. Diesel-hydraulic systems have long…

Robotics · Computer Science 2025-12-30 Mehdi Heydari Shahna

This paper presents a unified framework to analyze the manipulability and compliance of modular soft-rigid hybrid robotic fingers. The approach applies to both hydraulic and pneumatic actuation systems. A Jacobian-based formulation maps…

Robotics · Computer Science 2025-07-02 Jianshu Zhou , Boyuan Liang , Junda Huang , Masayoshi Tomizuka

Robot arms have been using in different systems, which the control of designed in desired trajectory is the main task. Also, it is anticipated that while in operation the developed 2DoF robot arm will be constantly encountered with noises…

Systems and Control · Electrical Eng. & Systems 2020-10-27 Mehran Rahmani , Asif Al Zubayer Swapnil , Ivan Rulik

In this paper, we introduce a novel approach to centroidal state estimation, which plays a crucial role in predictive model-based control strategies for dynamic legged locomotion. Our approach uses the Koopman operator theory to transform…

Robotics · Computer Science 2024-10-08 Shahram Khorshidi , Murad Dawood , Maren Bennewitz

Operating robots precisely and at high speeds has been a long-standing goal of robotics research. Balancing these competing demands is key to enabling the seamless collaboration of robots and humans and increasing task performance. However,…

This paper presents an active learning strategy for robotic systems that takes into account task information, enables fast learning, and allows control to be readily synthesized by taking advantage of the Koopman operator representation. We…

Robotics · Computer Science 2019-06-13 Ian Abraham , Todd D. Murphey

This paper presents a model-based reinforcement learning (RL) framework for optimal closed-loop control of nonlinear robotic systems. The proposed approach learns linear lifted dynamics through Koopman operator theory and integrates the…

Robotics · Computer Science 2026-04-23 Wenjian Hao , Yuxuan Fang , Zehui Lu , Shaoshuai Mou

Soft robots are well suited for contact-rich tasks due to their compliance, yet this property makes accurate and tractable modeling challenging. Planning motions with dynamically-feasible trajectories requires models that capture arbitrary…

Robotics · Computer Science 2026-03-25 Beibei Liu , Akua K. Dickson , Ran Jing , Andrew P. Sabelhaus

Handed Shearing Auxetics (HSA) are a promising structure for making electrically driven robots with distributed compliance that convert a motors rotation and torque into extension and force. We overcame past limitations on the range of…

Robotics · Computer Science 2021-10-05 Ian Good , Tosh Brown-Moore , Aditya Patil , Daniel Revier , Jeffrey Ian Lipton

Online optimal control of quadrupedal robots would enable them to plan their movement in novel scenarios. Linear Model Predictive Control (LMPC) has emerged as a practical approach for real-time control. In LMPC, an optimization problem…

Robotics · Computer Science 2025-07-22 Chun-Ming Yang , Pranav A. Bhounsule

We introduce the use of harmonic analysis to decompose the state space of symmetric robotic systems into orthogonal isotypic subspaces. These are lower-dimensional spaces that capture distinct, symmetric, and synergistic motions. For linear…

Deformable robots are notoriously difficult to model or control due to its high-dimensional configuration spaces. Direct trajectory optimization suffers from the curse-of-dimensionality and incurs a high computational cost, while…

Robotics · Computer Science 2023-11-06 Chen Liang , Xifeng Gao , Kui Wu , Zherong Pan

Soft and soft-rigid hybrid robots are inherently underactuated and operate under tight actuator limits, making task-space control with stability guarantees challenging. Common nonlinear strategies for soft robots (e.g., those based on PD…

Robotics · Computer Science 2026-03-09 Huy Pham , Zach J. Patterson

This paper reports a theory of Koopman operators for a class of hybrid dynamical systems with globally asymptotically stable periodic orbits, called hybrid limit-cycling systems. We leverage smooth structures intrinsic to the hybrid…

Dynamical Systems · Mathematics 2024-11-07 Natsuki Katayama , Yoshihiko Susuki

We propose a novel multi-section cable-driven soft robotic arm inspired by octopus tentacles along with a new modeling approach. Each section of the modular manipulator is made of a soft tubing backbone, a soft silicon arm body, and two…

Robotics · Computer Science 2024-05-17 Xinda Qi , Yu Mei , Dong Chen , Zhaojian Li , Xiaobo Tan

This paper presents the mechatronic design, dynamic modeling, and experimental validation of a three-degree-of-freedom (3-DOF) micro parallel robot featuring a prismatic-spherical (3PS) topology actuated by three Hydraulically Amplified…

Optimization and Control · Mathematics 2026-04-29 Agustin Feregrino , Nelson Cisneros , Alexis Lefèvre , Yongxin Wu , Yann Le Gorrec
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