Underactuated tendon-driven hands offer compact actuation and passive compliance, but tendon elongation under restoring-spring loading introduces configuration-dependent joint deviations. This paper presents H-PAC, a modular 6-actuator, 15-DoF robotic hand with a control-oriented modeling and implementation framework. A sparse analytical actuator-joint model is derived from the tendon-routing geometry, and a mechanics-based compensation model is developed to account for tendon-elasticity-induced joint errors. The proposed method is implemented in a hierarchical architecture: a host computer performs workspace-constrained posture mapping and compensation, while an ESP32 generates synchronized commands for six position-controlled servos. The same control parameters and execution strategy are used across all tasks without task-specific retuning. Monotonic servo-sweep experiments show that the compensation substantially improves joint-angle prediction. The MAE of the index DIP joint decreases from 1.15 degrees to 0.18 degrees, and all nine evaluated joints achieve an MAE below 0.23 degrees. Representative postures and grasping configurations are further executed using the same control pipeline without external joint or force sensing in the control loop. The results demonstrate a practical approach to improving posture reproducibility in compact underactuated robotic end-effectors.
Tendon-cable transmission can reduce distal-limb inertia in full-size humanoids, but its elasticity, hysteresis, backlash, and multi-joint coupling introduce state-dependent joint-to-motor discrepancies. We present a hierarchical whole-body tracking framework for the 28-DoF Droid X3 that separates high-level motion learning from transmission compensation. A reference-residual policy is trained in simulation by single-stage proximal policy optimization (PPO) using a unified robot-space motion representation, globally anchored tracking rewards, hierarchical hard-example sampling, and tendon-oriented domain randomization. In simulation checkpoint evaluation, more than 90% of 12,674 tested reference motions are completed. Independently, a state-conditioned mapper is trained offline through a differentiable motor–joint forward model identified from physical motor-excitation data and connected in series between the frozen policy and the low-level motor controller. Randomized repeated Mapping-OFF/ON trials are conducted on two nominally identical Droid X3 units. Within every robot–motion block, the frozen PPO checkpoint, reference trajectory, controller settings, safety bounds, and frozen mapper weights are held fixed; complete trials are the statistical units. OFF converts desired joint positions with the robot-specific fixed static calibration, whereas ON feeds the complete policy-level desired-joint vector and measured plant state to the frozen mapper, which directly outputs the complete motor-position command. Across the complete physical trials, the aggregate action-completion rate is 68% with Mapping OFF and 79% with Mapping ON, an increase of 11 percentage points. Representative walk, squat, and dance trajectories illustrate lower tracking errors under Mapping ON, while individual frames and selected temporal fragments are used only for visualization.
Wencong Gan, Jie-Hui Chen, Qingdu Li et al.· Biomimetics· 0 citations
Replicating the kinematic complexity of the human thumb carpometacarpal (CMC) joint in robotic hands requires a careful balance between dexterity and mechanical simplicity. Most existing designs approximate the CMC joint with only two degrees of freedom (DOFs), which limits thumb opposition and grasp stability. This paper presents a compliant underactuated CMC joint that enables three rotational DOFs motion, specifically flexion, adduction, and passive internal rotation, using only two independently actuated tendons. A parametric spiral spring is integrated along the internal rotation axis to introduce controllable compliance, while a spatial V-groove tendon routing scheme generates motion coupling between the active and passive DOFs. Unlike rigid kinematic designs, the proposed compliant architecture allows the thumb to passively reorient upon object contact, thereby enlarging the functional workspace and enhancing opposition without requiring complex control strategies. A static model based on the principle of virtual work is developed to characterize the equilibrium behavior of the coupled mechanism. Experimental results confirm that the proposed joint achieves the desired 3-DOF motion with reduced actuation complexity, validating the effectiveness of the passive opposition mechanism.
To improve torque capacity and energy efficiency of humanoid ankles, this paper proposes a 2-DoF parallel elastic actuator (PEA). The main novelty of the proposed design lies in its dual-cam, single-gas-spring architecture, which enables torque compensation in both pitch and roll using a shared elastic element, thereby improving structural compactness compared with conventional multi-element compensation schemes. By leveraging parallel gas springs and customized cam modules, the proposed architecture provides dual-axis torque assistance tailored to specific task requirements. The second key contribution is the formulation of a coupled 2-DoF mathematical model that explicitly captures the interdependence between the two compensation units through the shared spring. Based on this model, an optimization-based design framework is developed to synthesize customized cam profiles from prescribed torque references, establishing a systematic link from task requirements to hardware realization. The complete lower-leg CAD integration is presented in detail. Static FEA and kinematic simulations confirm the design’s feasibility and torque-relief effectiveness. The results highlight the proposed design as a compact, customizable solution for 2-DoF humanoid ankle torque compensation.
Robotic systems operating in unstructured environments face a conflicting requirement: they must be manoeuvrable to easily navigate tight and unstructured environments, yet stiff enough to perform precise, heavy-duty tasks. Tendon-driven hyper-redundant manipulators offer a compelling solution due to their lightweight and slender designs. In this work, a novel stiffness-adjusting strategy for a tendon-driven manipulator is presented. It goes beyond complex antagonistic drives, joint-level added components, and passive high-pretension schemes. This approach utilises a single active tensioning system to modulate a global reference tension, providing on-demand stiffness adjustment on the robot. Through a systematic experimental campaign involving varying payloads and tension levels, the behaviour of a five-degree-of-freedom prototype is characterised using high-precision motion capture. The results demonstrate that this mechanically simple input can amplify tip stiffness by a factor of three to four. Furthermore, the presented compliance model validates the efficiency of the global tensioning strategy in modulating the system’s overall stiffness.
A. Poka, Federico Manara, D. Ludovico et al.· 2026 IEEE/ASME International...· 0 citations
Dexterous in-hand manipulation is becoming increasingly important as robotic systems evolve toward agile, general-purpose automation. This paper presents a decentralized, grid-based trajectory planning approach for in-hand manipulation that coordinates the eleven degrees of freedom of a fully pneumatically actuated anthropomorphic robotic hand. The planner uses uniform time discretization and continuously differentiable second-order point-to-point trajectories in position and velocity, which allows intuitive manual tuning of coordinated multi-actuator motions. Experimental validation on two in-hand manipulation tasks demonstrates smooth, highly dynamic, and repeatable execution of complex ball rotations, despite the lack of sensing for the soft finger actuators and object pose estimation. The results are achieved by combining feedforward control of the soft finger actuators with feedback control of the rigid palm actuators considering friction compensation. The proposed trajectory planning approach is generalizable and transferable to robots with parallel kinematics and partially observed states.
Samuel Pilch, C. Ebert, Artem Beger et al.· 2026 IEEE/ASME International...· 0 citations
Soft actuators enable dexterous and compliant interaction, but closed-loop task-space control remains challenging due to strong nonlinearities, distributed deformation, and uncertainty in their dynamics. This paper presents a real-time dynamic-model-based task-space feedback and estimation framework based on a non-minimal coordinate discrete elastic rod model formulated in absolute coordinates with holonomic constraints. The resulting structure preserves distributed mechanics while maintaining computational efficiency through sparse system matrices, enabling real-time control with up to 10 discretized rods. A quasi-static feedforward inverse model is combined with a task-space PI controller and a dynamic observer that fuses measurement residuals as virtual forces, enabling full-state estimation from sparse sensing. The approach is experimentally validated on three planar pneumatic soft actuators with varying geometries. Across five tasks, including drawing the digits 0-9 across the workspace (3-18 mm/s tip speed), tracking periodic motion (up to 37 cm/s), cross-platform generalization, reduced sensing conditions, and real-time user-defined references, our method achieves 1.5-2.3 mm root mean square error (RMSE) for precision motions and 5.5-12.4 mm RMSE at 1-2 Hz. Results demonstrate that structured, non-minimal dynamic models can enable real-time, high-precision, moderate-bandwidth task-space control of planar soft pneumatic actuators in free space.
Nithin S. Kumar, Joshua Gaston, D. Rucker et al.· 0 citations
