Robert K. Katzschmann


Last Name

Katzschmann

First Name

Robert K.

ORCID

0000-0001-7143-7259

Organisational unit

09689 - Katzschmann, Robert / Katzschmann, Robert

Search Results

Publications1 - 10 of 124
  • Planar Modeling and Sim-to-Real of a Tethered Multimaterial Soft Swimmer Driven by Peano-HASELs
    Item type: Conference Paper
    Gravert, Stephan-Daniel; Michelis, Mike Yan; Rogler, Simon; et al. (2022)
    2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
    Soft robotics has the potential to revolutionize robotic locomotion, in particular, soft robotic swimmers offer a minimally invasive and adaptive solution to explore and preserve our oceans. Unfortunately, current soft robotic swimmers are vastly inferior to evolved biological swimmers, especially in terms of controllability, efficiency, maneuverability, and longevity. Additionally, the tedious iterative fabrication and empirical testing required to design soft robots has hindered their optimization. In this work, we tackle this challenge by providing an efficient and straightforward pipeline for designing and fabricating soft robotic swimmers equipped with electrostatic actuation. We streamline the process to allow for rapid additive manufacturing, and show how a differentiable simulation can be used to match a simplified model to the real deformation of a robotic swimmer. We perform several experiments with the fabricated swimmer by varying the voltage and actuation frequency of the swimmer's antagonistic muscles. We show how the voltage and frequency vary the locomotion speed of the swimmer while moving in liquid oil and observe a clear optimum in forward swimming speed. The differentiable simulation model we propose has various downstream applications, such as control and shape optimization of the swimmer; optimization results can be directly mapped back to the real robot through our sim-to-real matching.
  • Exploration of underwater life with an acoustically controlled soft robotic fish
    Item type: Journal Article
    Katzschmann, Robert K.; DelPreto, Joseph; MacCurdy, Robert; et al. (2018)
    Science Robotics
    Closeup exploration of underwater life requires new forms of interaction, using biomimetic creatures that are capable of agile swimming maneuvers, equipped with cameras, and supported by remote human operation. Current robotic prototypes do not provide adequate platforms for studying marine life in their natural habitats. This work presents the design, fabrication, control, and oceanic testing of a soft robotic fish that can swim in three dimensions to continuously record the aquatic life it is following or engaging. Using a miniaturized acoustic communication module, a diver can direct the fish by sending commands such as speed, turning angle, and dynamic vertical diving. This work builds on previous generations of robotic fish that were restricted to one plane in shallow water and lacked remote control. Experimental results gathered from tests along coral reefs in the Pacific Ocean show that the robotic fish can successfully navigate around aquatic life at depths ranging from 0 to 18 meters. Furthermore, our robotic fish exhibits a lifelike undulating tail motion enabled by a soft robotic actuator design that can potentially facilitate a more natural integration into the ocean environment. We believe that our study advances beyond what is currently achievable using traditional thruster-based and tethered autonomous underwater vehicles, demonstrating methods that can be used in the future for studying the interactions of aquatic life and ocean dynamics.
  • Flying Hydraulically Amplified Electrostatic Gripper System for Aerial Object Manipulation
    Item type: Conference Paper
    Tscholl, Dario; Gravert, Stephan-Daniel; Appius, Aurel X.; et al. (2023)
    Springer Proceedings in Advanced Robotics ~ Robotics Research
    Rapid and versatile object manipulation in air is an open challenge. An energy-efficient and adaptive soft gripper combined with an agile aerial vehicle could revolutionize aerial robotic manipulation in areas such as warehousing. This paper presents a bio-inspired gripper powered by hydraulically amplified electrostatic actuators mounted to a quadcopter that can interact safely and naturally with its environment. Our gripping concept is motivated by an eagle's foot. Our custom multi-actuator concept is inspired by a scorpion tail design (consisting of a base electrode with pouches stacked adjacently) and spider-inspired joints (classic pouch motors with a flexible hinge layer). A hybrid of these two designs realizes a higher force output under moderate deflections of up to 25. compared to single-hinge concepts. In addition, sandwiching the hinge layer improves the robustness of the gripper. For the first time, we show that soft manipulation in air is possible using electrostatic actuation. This study demonstrates the potential of untethered hydraulically amplified actuators in aerial robotic manipulation. Our proof of concept opens up the use of hydraulic electrostatic actuators in mobile aerial systems. (https://youtube.com/watch?v=7PmZ8C0Ji08)
  • Aquarium: A Fully Differentiable Fluid-Structure Interaction Solver for Robotics Applications
    Item type: Conference Paper
    Lee, Jeong Hun; Michelis, Mike Yan; Katzschmann, Robert K.; et al. (2023)
    2023 IEEE International Conference on Robotics and Automation (ICRA)
    We present Aquarium, a differentiable fluid-structure interaction solver for robotics that offers stable simulation, accurately coupled fluid-robot physics in two dimensions, and full differentiability with respect to fluid and robot states and parameters. Aquarium achieves stable simulation with accurate flow physics by directly integrating over the incompressible Navier-Stokes equations using a fully implicit Crank-Nicolson scheme with a second-order finite-volume spatial discretization. The fluid and robot physics are coupled using the immersed-boundary method by formulating the noslip condition as an equality constraint applied directly to the Navier-Stokes system. This choice of coupling allows the fluidstructure interaction to be posed and solved as a nonlinear optimization problem. This optimization-based formulation is then exploited using the implicit-function theorem to compute derivatives. Derivatives can then be passed to downstream gradient-based optimization or learning algorithms. We demonstrate Aquarium's ability to accurately simulate coupled fluid-robot physics with numerous 2D examples, including a cylinder in free stream and a soft robotic fish tail with hardware validation. We also demonstrate Aquarium's ability to provide analytical gradients by performing gradient-based shape-and-gait optimization of an oscillating diamond foil to maximize its generated thrust.
  • Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains
    Item type: Conference Paper
    Lingsch, Levi E.; Michelis, Mike Yan; de Bézenac, Emmanuel; et al. (2024)
    Proceedings of Machine Learning Research ~ Proceedings of the 41st International Conference on Machine Learning
    The computational efficiency of many neural operators, widely used for learning solutions of PDEs, relies on the fast Fourier transform (FFT) for performing spectral computations. As the FFT is limited to equispaced (rectangular) grids, this limits the efficiency of such neural operators when applied to problems where the input and output functions need to be processed on general non-equispaced point distributions. Leveraging the observation that a limited set of Fourier (Spectral) modes suffice to provide the required expressivity of a neural operator, we propose a simple method, based on the efficient direct evaluation of the underlying spectral transformation, to extend neural operators to arbitrary domains. An efficient implementation of such direct spectral evaluations is coupled with existing neural operator models to allow the processing of data on arbitrary non-equispaced distributions of points. With extensive empirical evaluation, we demonstrate that the proposed method allows us to extend neural operators to arbitrary point distributions with significant gains in training speed over baselines, while retaining or improving the accuracy of Fourier neural operators (FNOs) and related neural operators.
  • Sensor-Embedded Muscle for Closed-Loop Controllable Actuation in Proprioceptive Biohybrid Robots
    Item type: Journal Article
    Filippi, Miriam; Balciunaite, Aiste; Georgopoulou, Antonia; et al. (2025)
    Advanced Intelligent Systems
    Biohybrid robots are soft robots that exploit unique characteristics of biological cells and tissues for motion generation. Skeletal muscle tissue-based bioactuators respond to externally applied stimuli, such as electrical fields. However, current bioactuation systems rely on open-loop control strategies that lack knowledge of the actuator's state. The regulation of output force and position of biohybrid robots requires self-sensing control systems that combine bioactuators with sensors and control paradigms. Herein, a soft, fiber-shaped mechanical sensor based on a piezoresistive composite is proposed that efficiently integrates with engineered skeletal muscle tissue and senses its contracting states in a cell culture environment in the presence of applied electrical fields. After testing the sensor's insulation and biocompatibility, its sensitivity for typical strains (<1%) is characterized, and its ability is proven to detect motions from contractile skeletal muscle tissue constructs. Finally, it is shown that the sensor response can feed an autonomous control system, thus demonstrating the first proprioceptive biohybrid robot that senses and responds to its contraction state. In addition to inspiring implantable systems, biomedical models, and other bioelectronic devices, the proposed technology will confer biohybrids with decisional autonomy, thus driving the paradigm shift between bioactuators and intelligent biohybrid robots.
  • Autonomous Vision-based Rapid Aerial Grasping
    Item type: Working Paper
    Bauer, Erik; Cangan, Barnabas Gavin; Katzschmann, Robert K. (2022)
    arXiv
    In a future with autonomous robots, visual and spatial perception is of utmost importance for robotic systems. Particularly for aerial robotics, there are many applications where utilizing visual perception is necessary for any real-world scenarios. Robotic aerial grasping using drones promises fast pick-and-place solutions with a large increase in mobility over other robotic solutions. Utilizing Mask R-CNN scene segmentation (detectron2), we propose a vision-based system for autonomous rapid aerial grasping which does not rely on markers for object localization and does not require the size of the object to be previously known. With spatial information from a depth camera, we generate a point cloud of the detected objects and perform geometry-based grasp planning to determine grasping points on the objects. In real-world experiments, we show that our system can localize objects with a mean error of 3 cm compared to a motion capture ground truth for distances from the object ranging from 0.5 m to 2.5 m. Similar grasping efficacy is maintained compared to a system using motion capture for object localization in experiments. With our results, we show the first use of geometry-based grasping techniques with a flying platform and aim to increase the autonomy of existing aerial manipulation platforms, bringing them further towards real-world applications in warehouses and similar environments.
  • Dynamic Motion Control of Multi-Segment Soft Robots Using Piecewise Constant Curvature Matched with an Augmented Rigid Body Model
    Item type: Conference Paper
    Katzschmann, Robert K.; Della Santina, Cosimo; Toshimitsu, Yasunori; et al. (2019)
    2019 2nd IEEE International Conference on Soft Robotics (RoboSoft)
    Despite the emergence of many soft-bodied robotic systems, model-based feedback control for soft robots has remained an open challenge. This is largely due to the intrinsic difficulties in designing controllers for systems with infinite dimensions. This work extends our previously proposed formulation for the dynamics of a soft robot from two to three dimensions. The formulation connects the soft robot's dynamic behavior to a rigid-bodied robot with parallel elastic actuation. The matching between the two systems is exact under the hypothesis of Piecewise Constant Curvature. Based on this connection, we introduce a control architecture with the aim of achieving accurate curvature and bending control. This controller accounts for the natural softness of the system moving in three dimensions, and for the dynamic forces acting on the system. The controller is validated in a realistic simulation, together with a kinematic inversion algorithm. The paper also introduces a soft robot capable of three-dimensional motion, that we use to experimentally validate our control strategy. © 2019 IEEE
  • Leveraging Pretrained Latent Representations for Few-Shot Imitation Learning on an Anthropomorphic Robotic Hand
    Item type: Conference Paper
    Liconti, Davide; Toshimitsu, Yasunori; Katzschmann, Robert K. (2024)
    2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids)
    In the context of imitation learning applied to anthropomorphic robotic hands, the high complexity of the systems makes learning complex manipulation tasks challenging. However, the numerous datasets depicting human hands in various different tasks could provide us with better knowledge regarding human hand motion. We propose a method to leverage multiple large-scale task-agnostic datasets to obtain latent representations that effectively encode motion subtrajectories that we included in a transformer-based behavior cloning method. Our results demonstrate that employing latent representations yields enhanced performance compared to conventional behavior cloning methods, particularly regarding resilience to errors and noise in perception and proprioception. Furthermore, the proposed approach solely relies on human demonstrations, eliminating the need for teleoperation and, therefore, accelerating the data acquisition process. Accurate inverse kinematics for fingertip retargeting ensures precise transfer from human hand data to the robot, facilitating effective learning and deployment of manipulation policies. Finally, the trained policies have been successfully transferred to a real-world 23Dof robotic system.
  • A Rapid and Robust Tendon-Driven Robotic Hand for Human-Robot Interactions Playing Rock-Paper-Scissors
    Item type: Conference Paper
    Deng, Xiang; Weirich, Stefan; Katzschmann, Robert K.; et al. (2024)
    2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN)
    Rapid human-robot interactions require fast hardware platforms with minimal latency and high reliability. In response, we present a cost-effective, electrically actuated, tendon-driven robotic hand. This hand features a unique spool-free actuation mechanism that achieves a limit-to-limit flexion movement in less than 60 ms, matching human speed. To our knowledge, it is among the fastest electric motor tendon-driven robotic hands available today. The high speed of the robotic hand was successfully demonstrated in public by playing Rock Paper Scissors at a science fair. This research work outlines the design methodology and introduces a simulation-optimization framework that allows users to preview the motion of the hand, quantify the actuation performance, and customize the design parameters prior to fabrication. The proposed actuation mechanism, along with the simulation and optimization tools, illustrates design principles and computational methods applicable to other dynamic human-robot applications that require fast reaction times. The Dextra hand design is available at https://sensorsini.github.io/dextra-robot-hand.
Publications1 - 10 of 124