About

Daniel Bruder is an Assistant Professor in the Department of Mechanical Engineering at the University of Michigan. He holds a Ph.D. and M.S. in Mechanical Engineering from the University of Michigan, obtained in 2020, and a B.S. in Engineering Sciences from Harvard University, earned in 2013. His research focuses on the design, modeling, and control of soft and other non-traditional robotic systems, with the goal of creating robots capable of safely assisting humans in real-world unstructured environments. Bruder's work emphasizes advancing control, design, mechatronics, and robotics to develop innovative robotic solutions for complex, real-world applications.

Research topics

• Artificial Intelligence
• Computer Science
• Control engineering
• Engineering

Selected publications

• Whole-Body Proprioceptive Morphing: A Modular Soft Gripper for Robust Cross-Scale Grasping

  2026-04-07

  articleOpen access

  Biological systems, such as the octopus, exhibit cross-scale manipulation by adaptively reconfiguring their entire form, a capability that remains elusive in robotics. Conventional soft grippers, while compliant, are mostly constrained by a fixed global morphology, and prior shape-morphing efforts have been largely confined to localized deformations, failing to replicate this biological dexterity. Inspired by this natural exemplar, we introduce the paradigm of collaborative, wholebody proprioceptive morphing, realized in a modular soft gripper architecture. Our design is a distributed network of modular self-sensing pneumatic actuators that enables the gripper to reconfigure its entire topology, achieving multiple morphing states that are controllable to form diverse polygonal shapes. By integrating rich proprioceptive feedback from embedded sensors, our system can seamlessly transition from a precise pinch to a large envelope grasp. We experimentally demonstrate that this approach expands the grasping envelope and enhances generalization across diverse object geometries (standard and irregular) and scales (up to $10 \times$), while also unlocking novel manipulation modalities such as multi-object and internal hook grasping. This work presents a low-cost, easy-to-fabricate, and scalable framework that fuses distributed actuation with integrated sensing, offering a new pathway toward achieving biological levels of dexterity in robotic manipulation. The complete control software, communication protocols, and mechanical CAD files for the gripper are available at https://github.com/ethansab-bit/Soft_Gripper.

  PublisherOA PDFDOI

• Koopman Operators in Robot Learning

  IEEE Transactions on Robotics · 2026-01-01 · 1 citations

  article

  Koopman operator theory offers a rigorous treatment of dynamics, emerging as a robust alternative for learning-based control in robotics. By representing nonlinear dynamics as a linear, higher-dimensional operator, it provides a fresh lens for modeling complex systems. Its ability to support incremental updates and low computational cost makes it particularly appealing for real-time applications and online learning. This review delves deeply into the foundations, systematically bridging theoretical principles to practical robotic applications. We explain mathematical underpinnings, approximation approaches for inputs, data collection strategies, and lifting function design. We explore how Koopman models unify tasks like model-based control, state estimation, and motion planning. The review surveys cutting-edge research across domains ranging from aerial and legged platforms to manipulators, soft robots, and multi-agent networks. We also present advanced theoretical topics and reflect on open challenges and future research directions. To support adoption, we provide a hands-on tutorial with code at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/sunnyshi0310/KoopmanRobo/tree/main</uri>.

  PublisherDOI

• UMArm: Untethered, Modular, Portable, Soft Pneumatic Arm

  arXiv (Cornell University) · 2025-05-16

  preprintOpen accessSenior author

  Robotic arms are essential to modern industries, however, their adaptability to unstructured environments remains limited. Soft robotic arms, particularly those actuated pneumatically, offer greater adaptability in unstructured environments and enhanced safety for human-robot interaction. However, current pneumatic soft arms are constrained by limited degrees of freedom, precision, payload capacity, and reliance on bulky external pressure regulators. In this work, a novel pneumatically driven rigid-soft hybrid arm, ``UMArm'', is presented. The shortcomings of pneumatically actuated soft arms are addressed by densely integrating high-force-to-weight-ratio, self-regulated McKibben actuators onto a lightweight rigid spine structure. The modified McKibben actuators incorporate valves and controllers directly inside, eliminating the need for individual pressure lines and external regulators, significantly reducing system weight and complexity. Full untethered operation, high payload capacity, precision, and directionally tunable compliance are achieved by the UMArm. Portability is demonstrated through a wearable assistive arm experiment, and versatility is showcased by reconfiguring the system into an inchworm robot. The results of this work show that the high-degree-of-freedom, external-regulator-free pneumatically driven arm systems like the UMArm possess great potential for real-world unstructured environments.

  PublisherOA PDFDOI

• A Variable-Stiffness Robotic Link Based on Rotating-Rectangle Auxetic Structures for Safe Human-Robot Interaction

  IEEE Robotics and Automation Letters · 2025-09-16 · 1 citations

  articleSenior author

  Industrial robotics emphasizes the development of collaborative robots (co-bots) designed to work safely alongside human operators. To minimize the risk of injury during physical human-robot interactions (pHRI), it is common to incorporate compliance into the joints or links of robotic manipulators. This work introduces a variable-stiffness robot link with an auxetic tubular design capable of achieving stiffening through a constantlength shape-morphing process under vacuum. The constant length is maintained in compliant and stiffened states, allowing the same robot arm kinematic model to be used regardless of the stiffness mode. Based on the structure's geometric design and shape-morphing behavior, we develop a kinematic model and a design optimization algorithm. We construct a physical robot arm prototype consisting of two auxetic stiffening links to validate our approach. Experiments are conducted to evaluate the bending stiffness of the links and demonstrate how stiffening enhances the arm's robustness and payload capacity. Safety during interactions is ensured by maintaining compliance when the robot links are not stiffened, highlighting the adaptability of the proposed design.

  PublisherDOI

• An Enhanced Proprioceptive Method for Soft Robots Integrating Bend Sensors and IMUs

  ArXiv.org · 2025-11-03

  preprintOpen accessSenior author

  This study presents an enhanced proprioceptive method for accurate shape estimation of soft robots using only off-the-shelf sensors, ensuring cost-effectiveness and easy applicability. By integrating inertial measurement units (IMUs) with complementary bend sensors, IMU drift is mitigated, enabling reliable long-term proprioception. A Kalman filter fuses segment tip orientations from both sensors in a mutually compensatory manner, improving shape estimation over single-sensor methods. A piecewise constant curvature model estimates the tip location from the fused orientation data and reconstructs the robot's deformation. Experiments under no loading, external forces, and passive obstacle interactions during 45 minutes of continuous operation showed a root mean square error of 16.96 mm (2.91% of total length), a 56% reduction compared to IMU-only benchmarks. These results demonstrate that our approach not only enables long-duration proprioception in soft robots but also maintains high accuracy and robustness across these diverse conditions.

  PublisherOA PDFDOI

• A Koopman-based residual modeling approach for the control of a soft robot arm

  The International Journal of Robotics Research · 2024-10-08 · 13 citations

  article1st authorCorresponding

  Soft robots are challenging to model and control due to their poorly defined kinematics and nonlinear dynamics. Recently, Koopman operator theory has been shown capable of constructing control-oriented soft robot models from data. However, building these models requires extensive data collection and they do not necessarily generalize well outside of the training observations. This paper presents a more data-efficient and generalizable approach to soft robot modeling that first identifies a physics-based Koopman model then supplements it with a data-driven residual Koopman model. The resulting combined model is linear and thus compatible with real-time model-based control techniques such as Model Predictive Control (MPC). The efficacy of the approach is demonstrated on several simulated systems and on a real soft robot arm, where it is shown to generate models that are more accurate than purely physics-based models and require less data to construct than purely data-driven models. Using a model-based controller, the soft arm is able to successfully track end effector trajectories, perform a pick-and-place task, and write on a dry-erase board, showcasing the applicability of this framework to increase the capabilities of soft robotic systems.

  PublisherDOI

• Embedded Valves for Distributed Control of Soft Pneumatic Actuators

  2024-10-14

  articleSenior author

  Soft robotic systems are inherently compliant, giving them unique capabilities not possessed by traditional rigid-bodied robot systems. Many soft systems rely on soft pneumatic actuators. One of the biggest downsides of such actuators is the need for bulky pressure-regulating devices and individual pneumatic supply lines. In this work, a miniaturized pressure-regulating system is developed and embedded into the unused space inside of a soft pneumatic McKibben actuator, enabling the simultaneous pressure control of multiple actuators connected to a single pneumatic supply line. This "valve-embedded" actuator is capable of regulating its internal pressure within 0.05 psi of a desired set point, even under external load. Compared to a conventional McKibben actuator driven by external valves, the valve-embedded actuator is experimentally shown to consistently achieve faster settling times. To showcase the practical application of the valve-embedded actuator on a robotic system, a 0.9m serial-linked robot driven by five independently controlled valve-embedded actuators was assembled, and was shown to achieve an average root mean square error of less than 1.5cm in a waypoint tracking experiment. The miniaturized pressure control system developed in this work is open source and could be embedded in any fluid-driven actuator, enabling more capable and densely actuated pneumatic soft robots.

  PublisherDOI

• Koopman Operators in Robot Learning

  arXiv (Cornell University) · 2024-08-08 · 5 citations

  preprintOpen access

  Koopman operator theory offers a rigorous treatment of dynamics and has been emerging as an alternative modeling and learning-based control method across various robotics sub-domains. Due to its ability to represent nonlinear dynamics as a linear (but higher-dimensional) operator, Koopman theory offers a fresh lens through which to understand and tackle the modeling and control of complex robotic systems. Moreover, it enables incremental updates and is computationally inexpensive, thus making it particularly appealing for real-time applications and online active learning. This review delves deeply into the foundations of Koopman operator theory and systematically builds a bridge from theoretical principles to practical robotic applications. We begin by explaining the mathematical underpinnings of the Koopman framework and discussing approximation approaches for incorporating inputs into Koopman-based modeling. Foundational considerations, such as data collection strategies as well as the design of lifting functions for effective system embedding, are also discussed. We then explore how Koopman-based models serve as a unifying tool for a range of robotics tasks, including model-based control, real-time state estimation, and motion planning. The review proceeds to a survey of cutting-edge research that demonstrates the versatility and growing impact of Koopman methods across diverse robotics sub-domains: from aerial and legged platforms to manipulators, soft-bodied systems, and multi-agent networks. A presentation of more advanced theoretical topics, necessary to push forward the overall framework, is included. Finally, we reflect on some key open challenges that remain and articulate future research directions that will shape the next phase of Koopman-inspired robotics. To support practical adoption, we provide a hands-on tutorial with executable code at https://shorturl.at/ouE59.

  PublisherOA PDFDOI

• Modeling and Experimental Validation of High‐Flow Fluid‐Driven Membrane Valves for Hyperactuated Soft Robots

  Advanced Intelligent Systems · 2024-05-16 · 3 citations

  articleOpen access

  Herein, the design, modeling, and validation of high‐flow, fluid‐driven, membrane valves tailored specifically for applications in soft robotic systems are described. Targeting the piping problem in hyper‐actuated soft robots, two fluid‐driven membrane valve designs that can admit flows of up to while weighing less than are introduced. A mathematical model to predict fluid flow by representing the displacement of the membrane as a scalar quantity influenced by the balance of pressures applied across the valve's ports is established. The model incorporates six parameters with direct physical relevance, enhancing its usefulness in valve design and system integration. In an experimental validation, flow rates with deviations within 4% are predicted and the onset of flow is correctly identified with an error rate of less than 1%. In addition, applications of these valves for flow amplification and for the creation of a fluid‐driven oscillator are experimentally demonstrated. This research contributes to the advancement of soft robotics by providing a tool for designing, optimizing, and controlling fluid‐driven systems and it lays the groundwork for the future development of embedded, fluid‐controlled valve networks that can be used to realize hyper‐actuated soft robotic systems.

  PublisherOA PDFDOI

• Increasing the Payload Capacity of Soft Robot Arms by Localized Stiffening

  Zenodo (CERN European Organization for Nuclear Research) · 2023-07-28

  datasetOpen access1st authorCorresponding

  This is the data corresponding to the experiments in the Science Robotics paper entitled <em>Increasing the Payload Capacity of Soft Robot Arms by Localized Stiffening </em>by Daniel Bruder, Moritz A. Graule, Clark B. Teeple, and Robert J. Wood.

  PublisherDOI

Frequent coauthors

• Ram Vasudevan

  University of Michigan–Ann Arbor

  21 shared

• C. David Remy

  11 shared

• R. Brent Gillespie

  University of Michigan–Ann Arbor

  10 shared

• Xun Fu

  Robotics Research (United States)

  6 shared

• Sridhar Kota

  University of Michigan–Ann Arbor

  6 shared

• Audrey Sedal

  4 shared

• Talia Y. Moore

  University of Michigan–Ann Arbor

  3 shared

• Alison R. Davis Rabosky

  University of Michigan–Ann Arbor

  3 shared

Education

• PhD. candidate, Mechanical Engineering

  University of Michigan

  2020

• Bachelor of Science, School of Engineering and Applied Sciences

  Harvard University

  2013