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.

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.

Physical interaction in robotics predominantly relies on sensing, computation, and feedback control, resulting in increasing system complexity. Here, we introduce geometry-encoded actuation (GEA), a design framework that exploits multistable origami geometries as programmable physical interaction layers to mechanically encode environmental stimuli into autonomous structural responses. By integrating multistable origami with liquid crystal elastomer (LCE) actuators, GEA couples structural geometry, energy landscapes, and stimuli-responsive actuation to achieve programmable shape transformation without continuous sensing or complex control during the interaction process. A theoretical framework based on spherical geometry, energy analysis, and structural optimization is established to guide the design of bistable origami mechanisms. The proposed framework is validated through representative robotic systems, including a bistable origami gripper, an adaptive origami wheel, and autonomous locomotion and manipulation demonstrations, achieving up to 45% actuation strain, 106 ∘ programmable geometric transformation, 100% task success in 10 consecutive trials, and stable operation after 500 actuation cycles. By embedding interaction intelligence directly into structural geometry, GEA redistributes part of the interaction complexity from computation to morphology, providing a general framework for programmable physical intelligence and adaptive embodied robotic systems.

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.

Mapping the operator's six degrees of freedom (DoF) hand pose to the robotic system's end-effector with inverse kinematics is a common approach in pose-tracking teleoperation. However, this approach suffers from nonintuitive orientation motion mapping caused by robot-side singularities, especially when the robotic arm follows the Pieper kinematics structure. In this work, to address the challenges of teleoperation near kinematic singularities across different setups, we propose two selectively applied singularity-avoidant motion mapping strategies. One approach locks one DoF on the robot arm to avoid wrist singularity, while the other approach appends additional joints but preserves all six DoFs of the robotic end-effector. In addition, we introduce the concept of an operational Jacobian to formally analyze the smoothness of robotic joint motions under pose-tracking teleoperation. The proposed solution generalizes across various setups, including a global-local interface on a lightweight arm, a collaborative arm, and an industrial arm, as well as VR-based bimanual teleoperation on a humanoid robot, demonstrating smooth and stable singularity-avoidant teleoperation.

The manipulation of objects of varying sizes is crucial for grippers used in logistics and smart home applications. However, conventional grippers lack cross‐scale grasping capabilities. This study presents a novel single‐pump‐actuated transformable dual‐mode gripper (SATDG) that combines vacuum suction and granular jamming to effectively grasp objects ranging from 10 to 300 mm in size, covering a vast 30‐fold range, with forces from 0.5 to 21 N. Using a bistable mechanism and origami structure, the SATDG can transform between two modes and activate them with one pressure source. Subsequently, a finite‐element analysis of the characteristics of the bistable frame and the origami structure is presented. The performances of both grasping modes of the SATDG are validated. Demonstrations have shown that the SATDG performs outstandingly in logistics and smart home scenarios.

In daily life, the human hand exhibits remarkable abilities, such as fine in-hand manipulation and multimodal sensing, which are crucial for complex tasks like multiobject manipulation. However, current robotic dexterous hands have not yet achieved this level of proficiency due to limitations in hardware, perception algorithms, control strategies, and data collection. In this work, we present a dexterous in-hand teleoperation framework, DIH-Tele, designed to enable such complex tasks. The framework includes the tactile dexterous hand (T-DexCo hand), an accurate dexterous teleoperation system, multimodal data collection, and an imitation learning algorithm based on discrete control space and fused training. The in-hand counting task is selected as a common example of multiobject manipulation, which involves counting a set of objects held in hand and selectively removing a specified number of them by in-hand manipulation. Our experimental results demonstrate that the DIH-Tele framework effectively leverages multimodal perception to perform multiobject manipulation tasks with a success rate approaching that of human teleoperation. Additionally, the learned fingertip behaviors are highly versatile, often utilizing every degree of freedom of the dexterous hand. Finally, ablation studies confirm the significant impact of multimodal perception and fused training on enhancing multiobject manipulation tasks.

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.

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, 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.

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.

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^∘ and 4^∘ 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

For objects with complex topological and geometrical features, stochastic topological grasping can be executed without the necessity for feedback or precise planning. However, this grasping method has two significant limitations. First, the technique's effectiveness is reduced when interacting with topologically and geometrically simple objects like spheres, cubes, and cylinders, due to the inherent variability in grasping patterns. Additionally, the method's low stiffness restricts its ability to securely handling heavier objects. To address these challenges, this paper proposes an entanglement soft robotic gripper with variable stiffness and two transformed grasping modes (entanglement and clamping modes). The gripper contains three filaments, which can enhance the stiffness through the mechanism of layer jamming. Furthermore, the entanglement mode and the clamping mode, can be transformed by adjusting the working length of the filaments. The grasping performance comparison with and without variable stiffness was carried out, and the results indicated that the implementation of variable stiffness led to a 149 % increase in payload weight. Through experimental validation, we successfully employed the gripper in variable stiffness and transformed modes to grasp items with various shapes and weights. Demonstration of grasping heavier objects and transforming between two grasping modes were also conducted to showcase the adaptability and versatility of the gripper.

Navigating complex environments-ranging from confined spaces to open areas blocked by obstacles or out of reach of traditional robotic arms-remains a major challenge for conventional robotic systems. These challenges include limited dexterity, adaptability, and accessibility. To address these issues, this article proposed a dexterity and exploration enhanced quadruped robot (DEEQR), which was designed with a quadruped base and a multifunctional retractable variable stiffness manipulator (MRVSM). The MRVSM features a compact active-passive composite continuum mechanism with a 12.8 mm diameter, a high-stiffness telescopic mechanism, and a 2.5 mm diameter working channel that supports interchangeable tools such as a 2-DOF grasper or other miniaturized tools. This design enables precise manipulation and exploration in narrow and obstructed environments. Experiments demonstrated that DEEQR can traverse uneven terrain, navigate confined gaps, and perform complex tasks such as a bomb disposal operation inside a backpack with a 5 cm diameter entrance. The robot also successfully explored and delivered water in blocked spaces beyond the direct reach of the quadruped base. These results highlight DEEQR's strong potential for anti-terrorism applications and search and rescue missions in challenging environments.

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.

Robotic grasping and manipulation in underwater environments present unique challenges for robotic hands traditionally used on land. These challenges stem from dynamic water conditions, a wide range of object properties from soft to stiff, irregular object shapes, and varying surface frictions. One common approach involves developing finger-based hands with embedded compliance using underactuation and soft actuators. This study introduces an effective alternative solution that does not rely on finger-based hand designs. We present a fish mouth inspired origami gripper that utilizes a single degree of freedom to perform a variety of robust grasping tasks underwater. The innovative structure transforms a simple uniaxial pulling motion into a grasping action based on the Yoshimura crease pattern folding. The origami gripper offers distinct advantages, including scalable and optimizable design, grasping compliance, and robustness, with four grasping types: pinch, power grasp, simultaneous grasping of multiple objects, and scooping from the seabed. In this work, we detail the design, modeling, fabrication, and validation of a specialized underwater gripper capable of handling various marine creatures, including jellyfish, crabs, and abalone. By leveraging an origami and bio-inspired approach, the presented gripper demonstrates promising potential for robotic grasping and manipulation in underwater environments.

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 article 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: first, a variable-stiffness gripper capable of adaptive grasping, firm holding, and in-hand manipulation, and second, 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.

As one of the effective closed-loop control methods, visual servoing control methods are widely applied to continuum robots. However, existing visual servoing control methods mostly focus on accurate control of the robot's end-effector, with less consideration given to the robot's shape. In this work, a spatial position-based visual servoing obstacle-avoidable shape control framework for an 11-degree-of-freedom (DOF) hybrid continuum robot is proposed. In the control framework, a set of markers representing the shape of the continuum robot are measured and two spatial arcs are used to fit the shape. When controlling the redundant DOFs of the robot, position-based visual servoing shape control combined with obstacle avoidance is formulated as a quadratic programming problem, yielding the optimal solution at each sample time for the joint velocity vector of the 11-DOF hybrid continuum robot. Several experiments are conducted to validate the proposed control framework, which indicates the accuracy of the shape control achieves 0.88 mm. Index Terms-Continuum robots, shape control, position-based visual servo control, obstacle avoidance

Observing that the key for robotic action planning is to understand the target-object motion when its associated part is manipulated by the end effector, we propose to generate the 3D object-part scene flow and extract its transformations to solve the action trajectories for diverse embodiments. The advantage of our approach is that it derives the robot action explicitly from object motion prediction, yielding a more robust policy by understanding the object motions. Also, beyond policies trained on embodiment-centric data, our method is embodiment-agnostic, generalizable across diverse embodiments, and being able to learn from human demonstrations. Our method comprises three components: an object-part predictor to locate the part for the end effector to manipulate, an RGBD video generator to predict future RGBD videos, and a trajectory planner to extract embodiment-agnostic transformation sequences and solve the trajectory for diverse embodiments. Trained on videos even without trajectory data, our method still outperforms existing works significantly by 27.7% and 26.2% on the prevailing virtual environments MetaWorld and Franka-Kitchen, respectively. Furthermore, we conducted real-world experiments, showing that our policy, trained only with human demonstration, can be deployed to various embodiments.

Grasping objects across vastly different sizes and physical states-including both solids and liquids-with a single robotic gripper remain 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 submillimeter scale 0.2 mm2 (glass bead) to over 62,000 mm2 (A4-sized paper and woven bag), enabling manipulation of objects nearly 3500× smaller and 88× 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 Tactile-Inferred Grasping Mode Selection 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.