arXiv subjects

Jianshu Zhou

Publications and source records attributed to Jianshu Zhou.

At least 19 recordsLinked to original sources

arXiv2026

MagCilia: A Compact Magnetociliary Tactile Sensor with 3D Force Sensing for Robotic Contact Perception and Grasping Feedback

Robotic grasping and surface exploration benefit from simultaneous measurement of normal and tangential forces and from surface information obtained through contact. Here, we present a compact magnetociliary tactile sensor (MagCilia) that combines a flexible magnetic-cilia structure with a Hall sensor for 3D force sensing. Quasi-static finite element analysis is used to investigate structural deformation and magnetic responses under multidirectional loading. To reconstruct forces from the coupled magnetic channels, we propose causal history fusion regression (CHFR), which combines current magnetic-field measurements with their recent changes. Five-fold cross-validation grouped by calibration record yields root-mean-square errors of 0.40, 0.57, and 0.69 N for Fx, Fy, and Fz, respectively, with corresponding coefficients of determination of 0.93, 0.90, and 0.92. Robotic experiments demonstrate tangential-force-guided gripper adjustment and multi-axis load monitoring under external perturbations. Frequency-domain features of the reconstructed forces distinguish six surface categories with 99.39% accuracy in three-fold cross-validation grouped by acquisition session. An online robotic demonstration additionally identifies all six tested surfaces. These results demonstrate 3D force reconstruction, grasping feedback, and surface recognition using a single compact tactile unit.

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arXiv2026

UMI-WM: Interactive World Modeling for Bimanual Manipulation from Wrist Cameras

Universal Manipulation Interface (UMI) pairs wrist-camera videos with camera trajectories and gripper openings, providing scalable, robot-free data for learning how bimanual actions change a shared scene. Existing interactive world models, however, typically rely on text or single-camera control and struggle to follow each arm's actions while maintaining consistency between the two wrist views. We introduce UMI-WM, an action-conditioned bimanual world model that jointly generates both wrist views from observation history, per-arm relative camera poses, and gripper widths, with cross-view consistency, action following, and visual fidelity. UMI-WM injects each arm's actions into its own wrist stream, while sparse hub tokens exchange scene information between the two streams. Camera-aware geometry conditioning and camera-guided noise further support per-view motion control and cross-view geometric exchange. We adapt the model for block-causal generation and distill the 30-step Teacher into a Student with four denoising steps. We train UMI-WM on self-collected open-world and in-studio UMI recordings combined with public manipulation data. To evaluate action following, we use an inverse dynamics model (IDM) to recover relative camera poses and gripper widths from generated videos, and introduce Bimanual Action Fidelity (BAF) to measure how closely these recovered actions match the conditioning actions. UMI-WM achieves the best results on all five reference-video fidelity metrics and the best or competitive performance on VBench and WorldArena. The Teacher and Student outperform all compared baselines in action following, while the four-step Student generates paired wrist videos 6.9 times faster than the Teacher.

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arXiv2026

A Deployable Four-Finger Payload for Teleoperated Free-Flying Manipulation with Astrobee

This article presents a bimanual teleoperation pipeline and conceptual design of a deployable four-finger payload for intra-vehicular free-flyers. Future habitats in low-Earth orbit (LEO) will require systems to perform mundane tasks like cargo handling and maintenance during crewed and uncrewed periods. The gripper payload provides 17 manipulation degrees-of-freedom (DoF) through four independently actuated fingers on a linear rail system. To control it, a virtual reality (VR) device interface maps the human ground operator's hand motions to the finger pairs, their separation to the rail, and common wrist motion to Astrobee translation. We present the preliminary results of teleoperating Astrobee in a custom zero-gravity MuJoCo-based International Space Station (ISS) simulator through ten repeated trials of transporting a rigid ISS Cargo Transfer Bag (CTB). We measure task success, continuous contact retention, completion time, and cargo motion.

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arXiv2026

A Reconfigurable Dual-Opposition Architecture for Single-Hand Assembly and Manipulation

In-hand assembly is constrained by the need to maintain grasps on two separate parts while controlling their relative motion within a single hand. To enable both in-hand assembly and manipulation, we present a reconfigurable dual-opposition architecture. Specifically, to support simultaneous grasping of two parts and coordinated in-hand manipulation, four independently actuated fingers are organized into two virtual finger (VF) oppositions, with their relative configuration controlled by a reconfigurable palm. To describe hand motion and simultaneous two-object grasping configurations, a kinematic model of the fingers and palm and an object-size-conditioned workspace formulation are built. To further evaluate motion performance and assembly capability, finger-joint motion and palm tracking are characterized, and in-hand assembly is demonstrated through tasks involving grasping, alignment, fastening, and pressing. Ablation experiments further demonstrate the importance of finger abduction/adduction and palm reconfiguration for successful in-hand assembly. In simulation, the proposed hand achieves a mean continuous sphere rotation success rate of 98.6% over diameters of 40-230 mm, compared with 73.8% for the LEAP Hand. After policy fine-tuning with external disturbances, the proposed hand achieves 92.8% success under disturbances from multiple directions, compared with 45.2% for the LEAP Hand. Hardware demonstrations further show in-hand rotation of objects of different sizes using policies trained in simulation. Together, these results show that the proposed architecture supports both assembly of two separately held parts and coordinated manipulation of a single object within one hand.

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arXiv2026

SpectraTac: A Compact Camera-Free Optical Tactile Sensor with Distributed Color Sensing

Tactile sensing is essential for physical interaction in robotics and human--machine systems. However, combining rich tactile information with compact hardware, low cost, and low computational overhead remains challenging. This work presents SpectraTac, a compact, camera-free optical tactile sensor that combines active red--green--blue (RGB) illumination with spatially distributed color sensing. Contact deforms a compliant transparent elastomer and modulates its internal light field, producing spatially differentiated changes in color and intensity. Three distributed color sensors capture these responses as low-dimensional spatio-spectral features, avoiding cameras, imaging optics, and high-dimensional image processing. The device measures 19.2 mm in diameter and 4 mm in height, with a material cost below USD~5. A data-driven decoding framework simultaneously estimates three-dimensional (3D) force and the contact region from the optical measurements. For 3D force prediction, the sensor achieved mean absolute errors (MAEs) of 0.161, 0.164, and 0.429 N along the x-, y-, and z-axes, respectively. The nine-region contact-classification accuracy was 99.9%. We further evaluated real-time 3D force tracking and contact-region-based human--machine interaction through an interactive control task. These results indicate that distributed color-resolved optical sensing offers a compact, low-cost alternative to camera-based tactile sensing for robotics, wearable sensing, and interactive systems.

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arXiv2026

LARC: Lazy Adaptive Reachability Certification of Robot Manipulator Trajectories

Discrete trajectory checks can miss collisions between sampled robot states. Reachability-based certification bounds motion between states, but uniform time partitions waste computation where clearance is large. We present lazy adaptive reachability certification (LARC), which checks a planned trajectory by bisecting only intervals with an inconclusive clearance test. For piecewise-cubic Hermite joint trajectories, the method bounds link occupancy using midpoint capsules inflated by exact componentwise speed maxima. Certified intervals covering the trajectory provide continuous-time external-obstacle clearance, subject to geometric containment, static obstacles, and a prescribed margin. On 160 AgileX PIPER trajectories from 80 start-goal pairs, LARC matched all decisions of the fixed-fine baseline at depth nine. It used 20328 interval evaluations (24.8% of baseline work), with a median paired speedup of 10.28x. A separate MoveIt/FCL audit checked 158051 states and detected collisions in 21 direct-interpolation controls, none of which LARC certified. The method reduced computation under a shared certificate model, but 27 of 139 sampled-clear trajectories remained uncertified. The sampled audit cannot independently prove continuous-time clearance.

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arXiv2026

Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning

Physically grounded robot intelligence requires robots to perceive, reason about, and regulate their interactions with the physical world. This capability is particularly critical in contact-sensitive manipulation, where successful task execution depends not only on visual perception and motion generation, but also on force regulation and adaptive control. In this context, recent robot learning methods have made substantial progress by integrating force, tactile, vision, language, and proprioceptive sensing into learned manipulation policies. In parallel, many systems adopt multi-phase architectures that combine high-level policies, action-refinement modules, and low-level controllers to bridge semantic task understanding with reactive physical execution. Despite these advances, existing surveys have not explicitly reviewed force- and tactile-aware robot learning from a unified perspective that jointly captures multimodal sensing and multi-phase system design. This survey addresses this gap by proposing TF-ART, a Tactile/Force-Aware Robot learning Taxonomy for multimodal and multi-phase frameworks, which maps individual methods into a unified hierarchical structure. The framework characterizes how recent works organize observation modalities, encode and fuse heterogeneous sensory inputs, generate and refine actions across multiple phases, and connect learned policies to reactive robot-end control. Building on this methodological view, we further examine the task settings and infrastructure requirements of physical interaction, thereby integrating both algorithmic and practical perspectives on force- and tactile-aware robot learning.

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arXiv2026

DexLink Hand: A Compact, Affordable, 16-DOF Linkage-Driven Hand with Human-Like Dexterity

Dexterous robotic hands face a longstanding trade-off among dexterity, compactness, and affordability. Particularly, high-degree-of-freedom designs typically demand complex actuation and transmission, hindering integration into human-scale forms. To address these challenges, this work presents a compact, low-cost linkage-driven anthropomorphic hand that achieves high dexterity, structural integration, and human-hand-like functionality. The hand integrates 20 joints driven by 16 independent actuators, with all actuation, sensing, and transmission components compactly embedded within a human-hand-sized structure. The resulting prototype weighs only 320g at a total cost below USD 400. To meet these objectives, a hybrid mechanical architecture combining planar and spatial linkage mechanisms is proposed, enabling decoupled multidirectional motion, biomimetic joint synergies, and high passive load-bearing capability. The thumb further incorporates biomimetic features supporting human-like reconfiguration and opposition movements. Through the coordinated integration of these mechanisms and structural layout, the prototype achieves a highly integrated design with anthropomorphic dexterity. Experimental evaluations demonstrate that the hand achieves the maximum Kapandji score, reproduces all 33 Feix grasp types, and performs stable grasping and dexterous manipulation across a wide variety of daily objects and tools. These results validate the proposed hand as an affordable, compact, and mechanically efficient platform for dexterous manipulation, teleoperation, and robot learning in human-centered environments.

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arXiv2026

SyLink Hand: A Synergy-Inspired Linkage-Driven Anthropomorphic Hand for Human-Like Dexterity

Designing anthropomorphic robotic hands that balance functional dexterity with mechanical simplicity remains a significant challenge. Inspired by human hand synergies, this paper presents the SyLink Hand, an anthropomorphic dexterous hand that integrates biomechanical synergy principles with linkage-driven transmission mechanisms to achieve a high degree of anthropomorphism in appearance, kinematics, and functionality within a compact and cost-effective architecture. Biomechanical analysis of natural hand motions using motion capture gloves reveals strong kinematic correlations among hand joints, providing the basis for a simplified yet functional degree-of-freedom (DOF) configuration. Guided by these synergistic characteristics, optimized linkage mechanisms are employed to coordinate multiple joint motions and reproduce natural finger trajectories. A novel spherical four-bar linkage is further proposed to achieve decoupled flexion/extension (Flex/Ext) and abduction/adduction (Abd/Add) at the metacarpophalangeal joint within a compact form factor. The resulting prototype integrates 19 joints driven by 11 actuators, with a total mass of 520g and a manufacturing cost of approximately USD 400. Experimental evaluations demonstrate its human-like kinematic performance, high load-bearing capability, and versatile grasping and manipulation skills. These results validate that the synergy-inspired, linkage-based design effectively balances anthropomorphism, mechanical simplicity, and functional versatility, highlighting its potential for practical deployment in dexterity-demanding robotic applications.

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arXiv2026

HandCept: A Visual-Inertial Fusion Framework for Accurate Proprioception in Dexterous Hands

As robotics progresses toward general manipulation, dexterous hands are becoming increasingly critical. However, proprioception in dexterous hands remains a bottleneck due to limitations in volume and generality. In this work, we present HandCept, the first visual-inertial proprioception framework designed to overcome the challenges of traditional joint angle estimation methods for dexterous hands. HandCept addresses the difficulty of achieving accurate and robust joint angle estimation in dynamic environments where both visual and inertial measurements are prone to noise and drift. It leverages a zero-shot learning approach using a wrist-mounted RGB-D camera and 9-axis IMUs, fused in real time via a latency-free Extended Kalman Filter (EKF). Our results show that HandCept achieves joint angle estimation errors generally between $2^{\circ}$ and $4^{\circ}$ without observable drift, outperforming visual-only and inertial-only methods. Furthermore, we validate the stability and uniformity of the IMU system, demonstrating that a common base frame across IMUs simplifies system calibration. To support sim-to-real transfer, we also open-source our high-fidelity rendering pipeline, which is essential for training without real-world ground truth. This work offers a robust, generalizable solution for proprioception in dexterous hands, with significant implications for robotic manipulation and human-robot interaction. https://github.com/huangjund/blenderYCB

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arXiv2026

Hamilton--Jacobi Reachability for Spacecraft Collision Avoidance

This article presents a Hamilton--Jacobi (HJ) reachability framework for a two--satellite collision avoidance problem operating in the same circular orbit, where relative motion is modeled in the radial--tangential--normal (RTN) frame using planar Hill--Clohessy--Wiltshire (HCW) dynamics. We define the target state space as unsafe relative configurations in the orbit plane corresponding to minimum separation requirements consistent with Federal Communications Commission (FCC) orbital standards. The interaction between spacecraft is formulated as a zero--sum differential game, where Player 1 is the controlled satellite and Player 2 is modeled as a bounded adversarial disturbance with unknown intent. We present the HJ formulation and compute backward reachable sets that characterize relative states from which collision cannot be avoided under worst-case disturbances, while states outside this set admit provably collision-free trajectories. These reachable sets are integrated with supervisory hybrid control logic to determine when evasive maneuvers must be initiated, enabling mathematically grounded safety guarantees for scalability.

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arXiv2026

A Dexterous and Compliant Gripper With Soft Hydraulic Actuation for Microgravity Manipulation

Astrobee's existing one-degree-of-freedom (DOF) underactuated compliant claw gripper enables perching on the International Space Station (ISS), but provides limited capability for continuous dexterous manipulation. More complex microgravity tasks require an end-effector that can maintain stable contact while limiting disturbance to the free-flying base, since contact forces directly couple into base motion. This article presents the integration of DexCoHand, a dexterous and compliant two-finger, 6-DOF gripper, with the Astrobee free-flying robot for microgravity manipulation. The system is evaluated in MuJoCo using Astrobee's standard handrail perching sequence, including approach, perching, and subsequent pan and tilt motions. Compared with Astrobee's existing gripper, DexCoHand preserves the commanded pan and tilt motions while reducing unintended cross-axis base motion. Hardware experiments on Earth further demonstrate DexCoHand's dexterous manipulation capabilities and its potential for more adaptable intelligent manipulation tasks.

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arXiv2025

Everything-Grasping (EG) Gripper: A Universal Gripper with Synergistic Suction-Grasping Capabilities for Cross-Scale and Cross-State Manipulation

Grasping objects across vastly different sizes and physical states-including both solids and liquids-with a single robotic gripper remains a fundamental challenge in soft robotics. We present the Everything-Grasping (EG) Gripper, a soft end-effector that synergistically integrates distributed surface suction with internal granular jamming, enabling cross-scale and cross-state manipulation without requiring airtight sealing at the contact interface with target objects. The EG Gripper can handle objects with surface areas ranging from sub-millimeter scale 0.2 mm2 (glass bead) to over 62,000 mm2 (A4 sized paper and woven bag), enabling manipulation of objects nearly 3,500X smaller and 88X larger than its own contact area (approximated at 707 mm2 for a 30 mm-diameter base). We further introduce a tactile sensing framework that combines liquid detection and pressure-based suction feedback, enabling real-time differentiation between solid and liquid targets. Guided by the actile-Inferred Grasping Mode Selection (TIGMS) algorithm, the gripper autonomously selects grasping modes based on distributed pressure and voltage signals. Experiments across diverse tasks-including underwater grasping, fragile object handling, and liquid capture-demonstrate robust and repeatable performance. To our knowledge, this is the first soft gripper to reliably grasp both solid and liquid objects across scales using a unified compliant architecture.

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arXiv2025

Global-Local Interface for On-Demand Teleoperation

Teleoperation is a critical method for human-robot interface, holds significant potential for enabling robotic applications in industrial and unstructured environments. Existing teleoperation methods have distinct strengths and limitations in flexibility, range of workspace and precision. To fuse these advantages, we introduce the Global-Local (G-L) Teleoperation Interface. This interface decouples robotic teleoperation into global behavior, which ensures the robot motion range and intuitiveness, and local behavior, which enhances human operator's dexterity and capability for performing fine tasks. The G-L interface enables efficient teleoperation not only for conventional tasks like pick-and-place, but also for challenging fine manipulation and large-scale movements. Based on the G-L interface, we constructed a single-arm and a dual-arm teleoperation system with different remote control devices, then demonstrated tasks requiring large motion range, precise manipulation or dexterous end-effector control. Extensive experiments validated the user-friendliness, accuracy, and generalizability of the proposed interface.

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arXiv2025

Programmable Locking Cells (PLC) for Modular Robots with High Stiffness Tunability and Morphological Adaptability

Robotic systems operating in unstructured environments require the ability to switch between compliant and rigid states to perform diverse tasks such as adaptive grasping, high-force manipulation, shape holding, and navigation in constrained spaces, among others. However, many existing variable stiffness solutions rely on complex actuation schemes, continuous input power, or monolithic designs, limiting their modularity and scalability. This paper presents the Programmable Locking Cell (PLC)-a modular, tendon-driven unit that achieves discrete stiffness modulation through mechanically interlocked joints actuated by cable tension. Each unit transitions between compliant and firm states via structural engagement, and the assembled system exhibits high stiffness variation-up to 950% per unit-without susceptibility to damage under high payload in the firm state. Multiple PLC units can be assembled into reconfigurable robotic structures with spatially programmable stiffness. We validate the design through two functional prototypes: (1) a variable-stiffness gripper capable of adaptive grasping, firm holding, and in-hand manipulation; and (2) a pipe-traversing robot composed of serial PLC units that achieves shape adaptability and stiffness control in confined environments. These results demonstrate the PLC as a scalable, structure-centric mechanism for programmable stiffness and motion, enabling robotic systems with reconfigurable morphology and task-adaptive interaction.

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arXiv2025

Unified Manipulability and Compliance Analysis of Modular Soft-Rigid Hybrid Fingers

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 actuator inputs to joint and task-space responses. Hydraulic actuators are modeled under incompressible assumptions, while pneumatic actuators are described using nonlinear pressure-volume relations. The framework enables consistent evaluation of manipulability ellipsoids and compliance matrices across actuation modes. We validate the analysis using two representative hands: DexCo (hydraulic) and Edgy-2 (pneumatic). Results highlight actuation-dependent trade-offs in dexterity and passive stiffness. These findings provide insights for structure-aware design and actuator selection in soft-rigid robotic fingers.

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arXiv2025

Prismatic-Bending Transformable (PBT) Joint for a Modular, Foldable Manipulator with Enhanced Reachability and Dexterity

Robotic manipulators, traditionally designed with classical joint-link articulated structures, excel in industrial applications but face challenges in human-centered and general-purpose tasks requiring greater dexterity and adaptability. To address these challenges, we propose the Prismatic-Bending Transformable (PBT) Joint, a novel, scissors-inspired mechanism with directional maintenance capability that provides bending, rotation, and elongation/contraction within a single module. This design enables transformable kinematic chains that are modular, reconfigurable, and scalable for diverse tasks. We detail the mechanical design, optimization, kinematic and dynamic modeling, and experimental validation of the PBT joint, demonstrating its integration into foldable, modular robotic manipulators. The PBT joint functions as a single stock keeping unit (SKU), enabling manipulators to be constructed entirely from standardized PBT joints. It also serves as a modular extension for existing systems, such as wrist modules, streamlining design, deployment, transportation, and maintenance. Three joint sizes have been developed and tested, showcasing enhanced dexterity, reachability, and adaptability, particularly in confined and cluttered spaces. This work presents a promising approach to robotic manipulator development, providing a compact and versatile solution for operation in dynamic and constrained environments.

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arXiv2025

Few-shot Sim2Real Based on High Fidelity Rendering with Force Feedback Teleoperation

Teleoperation offers a promising approach to robotic data collection and human-robot interaction. However, existing teleoperation methods for data collection are still limited by efficiency constraints in time and space, and the pipeline for simulation-based data collection remains unclear. The problem is how to enhance task performance while minimizing reliance on real-world data. To address this challenge, we propose a teleoperation pipeline for collecting robotic manipulation data in simulation and training a few-shot sim-to-real visual-motor policy. Force feedback devices are integrated into the teleoperation system to provide precise end-effector gripping force feedback. Experiments across various manipulation tasks demonstrate that force feedback significantly improves both success rates and execution efficiency, particularly in simulation. Furthermore, experiments with different levels of visual rendering quality reveal that enhanced visual realism in simulation substantially boosts task performance while reducing the need for real-world data.

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