We introduce ViSkin, a biomechanically principled approach to simulate skin mechanics on personalized avatars. Our model captures the salient characteristics of human skin, i.e., nonlinear stretching properties, anisotropic stiffness, direction-dependent pre-stretch, and heterogeneous sliding behavior. In particular, we introduce a novel representation of Langer lines, which describe the distribution of principal material directions across the human body. We further propose an optimization-based approach for inferring spatially-varying pre-stretch from motion capture data. We implement our new model using a computationally efficient intrinsic representation that simulates skin as a two-dimensional Lagrangian mesh embedded in the three-dimensional body surface. We demonstrate our method on a diverse set of body models, shapes, and poses and compare to experimentally-obtained skin motion data. Our results indicate that our method produces smoother and more plausible skin deformations than a baseline method and shows good accuracy compared to real-world data.

Artificial muscles play a crucial role in musculoskeletal robotics and prosthetics to approximate the force-generating functionality of biological muscle. However, current artificial muscle systems are typically limited to either contraction or extension, not both. This limitation hinders the development of fully functional artificial musculoskeletal systems. We address this challenge by introducing an artificial antagonistic muscle system capable of both contraction and extension. Our design integrates non-stretchable electrohydraulic soft actuators (HASELs) with electrostatic clutches within an antagonistic musculoskeletal framework. This configuration enables an antagonistic joint to achieve a full range of motion without displacement loss due to tendon slack. We implement a synchronization method to coordinate muscle and clutch units, ensuring smooth motion profiles and speeds. This approach facilitates seamless transitions between antagonistic muscles at operational frequencies of up to 3.2 Hz. While our prototype utilizes electrohydraulic actuators, this muscle-clutch concept is adaptable to other non-stretchable artificial muscles, such as McKibben actuators, expanding their capability for extension and full range of motion in antagonistic setups. Our design represents a significant advancement in the development of fundamental components for more functional and efficient artificial musculoskeletal systems, bringing their capabilities closer to those of their biological counterparts.

General-purpose robots should possess humanlike dexterity and agility to perform tasks with the same versatility as us. A human-like form factor further enables the use of vast datasets of human-hand interactions. However, the primary bottleneck in dexterous manipulation lies not only in software but arguably even more in hardware. Robotic hands that approach human capabilities are often prohibitively expensive, bulky, or require enterprise-level maintenance, limiting their accessibility for broader research and practical applications. What if the research community could get started with reliable dexterous hands within a day? We present the open-source ORCA hand, a reliable and anthropomorphic 17-DoF tendon-driven robotic hand with integrated tactile sensors, fully assembled in less than eight hours and built for a material cost below 2,000 CHF. We showcase ORCA’s key design features such as popping joints, auto-calibration, and tensioning systems that significantly reduce complexity while increasing reliability, accuracy, and robustness. We benchmark the ORCA hand across a variety of tasks, ranging from teleoperation and imitation learning to zero-shot sim-to-real reinforcement learning. Furthermore, we demonstrate its durability, withstanding more than 10,000 continuous operation cycles—equivalent to approximately 20 hours—without hardware failure, the only constraint being the duration of the experiment itself. Video is here: youtu.be/kUbPSYMmOds. Design files, source code, and documentation are available at srl.ethz.ch/orcahand.

This paper presents the SABD hand, a 16-degree-of-freedom (DoF) robotic hand that departs from purely anthropomorphic designs to achieve an expanded grasp envelope, enable manipulation poses beyond human capability, and reduce the required number of actuators. This is achieved by combining the adduction/abduction (Add/Abd) joint of digits four and five into a single joint with a large range of motion. The combined joint increases the workspace of the digits by 400% and reduces the required DoFs while retaining dexterity. Experimental results demonstrate that the combined Add/Abd joint enables the hand to grasp objects with a side distance of up to 200 mm. Reinforcement learning-based investigations show that the design enables grasping policies that are effective not only for handling larger objects but also for achieving enhanced grasp stability. In teleoperated trials, the hand successfully performed 86% of attempted grasps on suitable YCB objects, including challenging non-anthropomorphic configurations. These findings validate the design’s ability to enhance grasp stability, flexibility, and dexterous manipulation without added complexity, making it well-suited for a wide range of applications.

We present a computational approach for modeling the mechanical behavior of flexible scaled sheet materials---3D-printed hard scales embedded in a soft substrate. Balancing strength and flexibility, these structured materials find applications in protective gear, soft robotics, and 3D-printed fashion. To unlock their full potential, however, we must unravel the complex relation between scale pattern and mechanical properties. To address this problem, we propose a contact-aware homogenization approach that distills native-level simulation data into a novel macromechanical model. This macro-model combines piecewise-quadratic uniaxial fits with polar interpolation using circular harmonics, allowing for efficient simulation of large-scale patterns. We apply our approach to explore the space of isohedral scale patterns, revealing a diverse range of anisotropic and nonlinear material behaviors. Through an extensive set of experiments, we show that our models reproduce various scale-level effects while offering good qualitative agreement with physical prototypes on the macro-level.

Stripe patterns are ubiquitous in nature and everyday life. While the synthesis of these patterns has been thoroughly studied in the literature, their potential to control the mechanics of structured materials remains largely unexplored. In this work, we introduce Differentiable Stripe Patterns---a computational approach for automated design of physical surfaces structured with stripe-shaped bi-material distributions. Our method builds on the work by Knöppel and colleagues [2015] for generating globally-continuous and equally-spaced stripe patterns. To unlock the full potential of this design space, we propose a gradient-based optimization tool to automatically compute stripe patterns that best approximate macromechanical performance goals. Specifically, we propose a computational model that combines solid shell finite elements with XFEM for accurate and fully-differentiable modeling of elastic bi-material surfaces. To resolve non-uniqueness problems in the original method, we furthermore propose a robust formulation that yields unique and differentiable stripe patterns. We combine these components with equilibrium state derivatives into an end-to-end differentiable pipeline that enables inverse design of mechanical stripe patterns. We demonstrate our method on a diverse set of examples that illustrate the potential of stripe patterns as a design space for structured materials. Our simulation results are experimentally validated on physical prototypes.

Soft robotics offers unique advantages in manipulating fragile or deformable objects, human-robot interaction, and exploring inaccessible terrain. However, designing soft robots that produce large, targeted deformations is challenging. In this paper, we propose a new methodology for designing soft robots that combines optimization-based design with a simple and cost-efficient manufacturing process. Our approach is centered around the concept of robotic skins---thin fabrics with 3D-printed reinforcement patterns that augment and control plain silicone actuators. By decoupling shape control and actuation, our approach enables a simpler and cost-efficient manufacturing process. Unlike previous methods that rely on empirical design heuristics for generating desired deformations, our approach automatically discovers complex reinforcement patterns without any need for domain knowledge or human intervention. This is achieved by casting reinforcement design as a nonlinear constrained optimization problem and using a novel, three-field topology optimization approach tailored to fabrics with 3D-printed reinforcements. We demonstrate the potential of our approach by designing soft robotic actuators capable of various motions such as bending, contraction, twist, and combinations thereof. We also demonstrate applications of our robotic skins to robotic grasping with a soft three-finger gripper and locomotion tasks for a soft quadrupedal robot.

Garments with the ability to provide kinesthetic force-feedback on-demand can augment human capabilities in a non-obtrusive way, enabling numerous applications in VR haptics, motion assistance, and robotic control. However, designing such garments is a complex, and often manual task, particularly when the goal is to resist multiple motions with a single design. In this work, we propose a computational pipeline for designing connecting structures between active components—one of the central challenges in this context. We focus on electrostatic (ES) clutches that are compliant in their passive state while strongly resisting elongation when activated. Our method automatically computes optimized connecting structures that efficiently resist a range of pre-defined body motions on demand. We propose a novel dual-objective optimization approach to simultaneously maximize the resistance to motion when clutches are active, while minimizing resistance when inactive. We demonstrate our method on a set of problems involving different body sites and a range of motions. We further fabricate and evaluate a subset of our automatically created designs against manually created baselines using mechanical testing and in a VR pointing study.

We introduce TacTiles, light (1.8g), low-power (130 mW), and small form-factor (1 cm3) electromagnetic actuators that can form a flexible haptic array to provide localized tactile feedback. Our novel hardware design uses a custom 8-layer PCB, dampening materials, and asymmetric latching, enabling two distinct modes of actuation: contact and pulse mode. We leverage these modes in Virtual Reality (VR) to render continuous contact with objects and the exploration of object surfaces and volumes with spatial haptic patterns. Results from a series of experiments show that users are able to localize feedback, discriminate between modes with high accuracy, and differentiate objects from haptic surfaces and volumes even without looking at them.

We introduce DextrES, a flexible and wearable haptic glove which integrates both kinesthetic and cutaneous feedback in a thin and light form factor (weight is less than 8g). Our approach is based on an electrostatic clutch generating up to 20 N of holding force on each finger by modulating the electrostatic attraction between flexible elastic metal strips to generate an electrically-controlled friction force. We harness the resulting braking force to rapidly render on-demand kinesthetic feedback. The electrostatic brake is mounted onto the the index finger and thumb via modular 3D printed articulated guides which allow the metal strips to glide smoothly. Cutaneous feedback is provided via piezo actuators at the fingertips. We demonstrate that our approach can provide rich haptic feedback under dexterous articulation of the user's hands and provides effective haptic feedback across a variety of different grasps. A controlled experiment indicates that DextrES improves the grasping precision for different types of virtual objects. Finally, we report on results of a psycho-physical study which identifies discrimination thresholds for different levels of holding force.