Abstract-- Human video demonstrations provide abundant training data for learning robot policies, but video alone cannot capture the rich contact signals critical for mastering manipulation. We introduce OSMO, an open-source wearable tactile glove designed for human-to-robot skill transfer . The glove features 12 three-axis tactile sensors across the fingertips and palm and is designed to be compatible with state-of-the-art hand-tracking methods for in-the-wild data collection. We demonstrate that a robot policy trained exclusively on human demonstrations collected with OSMO, without any real robot data, is capable of executing a challenging contact-rich manipulation task. On a real-world wiping task requiring sustained contact pressure, our tactile-aware policy achieves a 72% success rate, outperforming vision-only baselines by eliminating contact-related failure modes. We release complete hardware designs, firmware, and assembly instructions to support community adoption. Tactile sensing enables humans to excel at manipulation by providing real-time feedback about contact forces that vision alone cannot capture. Consider trying to dice a carrot from video alone; one cannot observe the nuanced force control that makes the task successful. Many different applied forces can result in nearly identical visual appearances, leaving critical information about force control invisible to vision.

Abstract-- T ask-space control of robotic fingers is a critical enabler of dexterous manipulation, as manipulation objectives are most naturally specified in terms of fingertip motions and applied forces rather than individual joint angles. While task-space planning and control have been extensively studied for larger, arm-scale manipulators, demonstrations of precise task-space trajectory tracking in compact, multi-DoF robotic fingers remain scarce. In this paper, we present the physical prototyping and experimental characterization of a three-degree-of-freedom, linkage-driven, series-parallel robotic finger with analytic forward kinematics and a closed-form Jacobian. A resolved motion rate control (RMRC) scheme is implemented to achieve closed-loop task-space trajectory tracking. We experimentally evaluate the fingertip tracking performance across a variety of trajectories, including straight lines, circles, and more complex curves, and report millimeter-level accuracy. T o the best of our knowledge, this work provides one of the first systematic experimental demonstrations of precise task-space trajectory tracking in a linkage-driven robotic finger, thereby establishing a benchmark for future designs aimed at dexterous in-hand manipulation. I. INTRODUCTION Task-space control is a cornerstone of modern robotics because it allows specifying and executing motions directly in terms of end-effector positions and orientations, which are quantities most relevant to manipulation tasks. In dexterous manipulation, we are rarely interested in individual joint angles; rather, we care about applying forces, displacements, and velocities at specific points on the fingertips or the grasped object.

This paper presents environment-adaptive pinch-lifting built on a slot-constrained adaptive linkage (SCAL) and instantiated in two complementary fingers: SCAL-R, a rotational-drive design with an active fingertip that folds inward after contact to form an envelope, and SCAL-L, a linear-drive design that passively opens on contact to span wide or weak-feature objects. Both fingers convert surface following into an upward lifting branch while maintaining fingertip orientation, enabling thin or low-profile targets to be raised from supports with minimal sensing and control. Two-finger grippers are fabricated via PLA-based 3D printing. Experiments evaluate (i) contact-preserving sliding and pinch-lifting on tabletops, (ii) ramp negotiation followed by lift, and (iii) handling of bulky objects via active enveloping (SCAL-R) or contact-triggered passive opening (SCAL-L). Across dozens of trials on small parts, boxes, jars, and tape rolls, both designs achieve consistent grasps with limited tuning. A quasi-static analysis provides closed-form fingertip-force models for linear parallel pinching and two-point enveloping, offering geometry-aware guidance for design and operation. Overall, the results indicate complementary operating regimes and a practical path to robust, environment-adaptive grasping with simple actuation.

Telemanipulation of deformable objects requires high precision and dexterity from the users, which can be increased by kinesthetic and tactile feedback. However, the object shape can change dynamically, causing ambiguous perception of its alignment and hence errors in the robot positioning. Therefore, the tilt angle and position classification problem has to be solved to present a clear tactile pattern to the user. This work presents a telemanipulation system for plastic pipettes consisting of a multi-contact haptic device LinkGlide to deliver haptic feedback at the users' palm and two tactile sensors array embedded in the 2-finger Robotiq gripper. We propose a novel approach based on Convolutional Neural Networks (CNN) to detect the tilt and position while grasping deformable objects. The CNN generates a mask based on recognized tilt and position data to render further multi-contact tactile stimuli provided to the user during the telemanipulation. The study has shown that using the CNN algorithm and the preset mask, tilt, and position recognition by users is increased from 9.67% using the direct data to 82.5%.

Abstract--T actile sensing holds great promise for enhancing manipulation precision and versatility, but its adoption in robotic hands remains limited due to high sensor costs, manufacturing and integration challenges, and difficulties in extracting expressive and reliable information from signals. In this work, we present a low-cost, easy-to-make, adaptable, and compact fingertip design for robotic hands that integrates multi-modal tactile sensors. We use strain gauge sensors to capture static forces and a contact microphone sensor to measure high-frequency vibrations during contact. These tactile sensors are integrated into a compact design with a minimal sensor footprint, and all sensors are internal to the fingertip and therefore not susceptible to direct wear and tear from interactions. From sensor characterization, we show that strain gauge sensors provide repeatable 2D planar force measurements in the 0-5 N range and the contact microphone sensor has the capability to distinguish contact material properties. We apply our design to three dexterous manipulation tasks that range from zero to full visual occlusion. Given the expressiveness and reliability of tactile sensor readings, we show that different tactile sensing modalities can be used flexibly in different stages of manipulation, solely or together with visual observations to achieve improved task performance. For instance, we can precisely count and unstack a desired number of paper cups from a stack with 100% success rate which is hard to achieve with vision only. More details and videos can be found in https://sites.google.com/view/tactilefingertip.

Abstract--Despite progress in both traditional dexterous grasping pipelines and recent Vision-Language-Action (VLA) approaches, the grasp execution stage remains prone to pose inaccuracies, especially in long-horizon tasks, which undermines overall performance. T o address this "last-mile" challenge, we propose T acRefineNet, a tactile-only framework that achieves fine in-hand pose refinement of known objects in arbitrary target poses using multi-finger fingertip sensing. Our method iteratively adjusts the end-effector pose based on tactile feedback, aligning the object to the desired configuration. We design a multi-branch policy network that fuses tactile inputs from multiple fingers along with proprioception to predict precise control updates. T o train this policy, we combine large-scale simulated data from a physics-based tactile model in MuJoCo with real-world data collected from a physical system. Comparative experiments show that pretraining on simulated data and fine-tuning with a small amount of real data significantly improves performance over simulation-only training. T o our knowledge, this is the first method to enable arbitrary in-hand pose refinement via multi-finger tactile sensing alone. Project website is available at https://sites.google.com/view/tacrefinenet

Accurate hand motion capture (MoCap) is vital for applications in robotics, virtual reality, and biomechanics, yet existing systems face limitations in capturing high-degree-of-freedom (DoF) joint kinematics and personalized hand shape. Commercial gloves offer up to 21 DoFs, which are insufficient for complex manipulations while neglecting shape variations that are critical for contact-rich tasks. We present FSGlove, an inertial-based system that simultaneously tracks up to 48 DoFs and reconstructs personalized hand shapes via DiffHCal, a novel calibration method. Each finger joint and the dorsum are equipped with IMUs, enabling high-resolution motion sensing. DiffHCal integrates with the parametric MANO model through differentiable optimization, resolving joint kinematics, shape parameters, and sensor misalignment during a single streamlined calibration. The system achieves state-of-the-art accuracy, with joint angle errors of less than 2.7 degree, and outperforms commercial alternatives in shape reconstruction and contact fidelity. FSGlove's open-source hardware and software design ensures compatibility with current VR and robotics ecosystems, while its ability to capture subtle motions (e.g., fingertip rubbing) bridges the gap between human dexterity and robotic imitation. Evaluated against Nokov optical MoCap, FSGlove advances hand tracking by unifying the kinematic and contact fidelity. Hardware design, software, and more results are available at: https://sites.google.com/view/fsglove.

Abstract-- Dexterous in-hand manipulation remains a foun-dational challenge in robotics, with progress often constrained by the prevailing paradigm of imitating the human hand. This anthropomorphic approach creates two critical barriers: 1) it limits robotic capabilities to tasks humans can already perform, and 2) it makes data collection for learning-based methods exceedingly difficult. Both challenges are caused by traditional force-closure which requires coordinating complex, multi-point contacts based on friction, normal force, and gravity to grasp an object. This makes teleoperated demonstrations unstable and amplifies the sim-to-real gap for reinforcement learning. In this work, we propose a paradigm shift: moving away from replicating human mechanics toward the design of novel robotic embodiments. We introduce the Suction Leap-Hand (SLeap Hand), a multi-fingered hand featuring integrated fingertip suction cups that realize a new form of suction-enabled dexterity. More importantly, this suction-based embodiment unlocks a new class of dexterous skills that are difficult or even impossible for the human hand, such as one-handed paper cutting and in-hand writing. Our work demonstrates that by moving beyond anthropomorphic constraints, novel embodiments can not only lower the barrier for collecting robust manipulation data but also enable the stable, single-handed completion of tasks that would typically require two human hands. Dexterous manipulation, the ability to reconfigure objects within a single hand, remains a grand challenge in robotics [1], [2]. The dominant paradigm for achieving this goal has been data-driven learning on anthropomorphic hands, an approach that has led to successes in grasping and reorientation [3], [4], [5].

Tactile sensing plays a fundamental role in enabling robots to navigate dynamic and unstructured environments, particularly in applications such as delicate object manipulation, surface exploration, and human-robot interaction. In this paper, we introduce a passive soft robotic fingertip with integrated tactile sensing, fabricated using a 3D-printed elastomer lattice with embedded air channels. This sensorization approach, termed fluidic innervation, transforms the lattice into a tactile sensor by detecting pressure changes within sealed air channels, providing a simple yet robust solution to tactile sensing in robotics. Unlike conventional methods that rely on complex materials or designs, fluidic innervation offers a simple, scalable, single-material fabrication process. We characterize the sensors' response, develop a geometric model to estimate tip displacement, and train a neural network to accurately predict contact location and contact force. Additionally, we integrate the fingertip with an admittance controller to emulate spring-like behavior, demonstrate its capability for environment exploration through tactile feedback, and validate its durability under high impact and cyclic loading conditions. This tactile sensing technique offers advantages in terms of simplicity, adaptability, and durability and opens up new opportunities for versatile robotic manipulation.

General-purpose robots should possess human-like 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: https://youtu.be/kUbPSYMmOds. Design files, source code, and documentation are available at https://srl.ethz.ch/orcahand.