The design of dexterous robotic hands has long been constrained by empirical paradigms that struggle to balance anthropomorphic fidelity with dynamic performance. This study aims to establish a systematic methodology that bridges this gap through data-driven inverse design. We construct a quantitative association map between design variables and performance metrics using a comprehensive dataset of existing dexterous hands, then apply this map to translate explicit high-frequency dynamic targets into an optimized hardware configuration. The analysis reveals that the dominant principles for high-speed performance—tendon-driven transmission, proximal actuation, and lightweight rigid structures—closely mirror the biomechanical architecture of the human hand. Guided by this convergence, we develop the Beyond Hand, a 20-degree-of-freedom (DoF) anthropomorphic hand that preserves human-scale dimensions. Standardized frequency-response tests across all 15 joints show magnitude attenuation below 3 dB at 14 Hz and cutoff frequencies clustered around 10 Hz. In rhythm-game and Tetris-style manipulation tasks, the hand maintains over 90% accuracy at actuation frequencies up to 12 Hz. These results demonstrate that a performance-driven pathway can systematically elevate the dynamic capabilities of humanoid dexterous hands, offering a scalable framework for biomimetic robotic design.

Existing quadruped robots typically place their center of mass (CoM) near the geometric center of the body to achieve structural symmetry and simplify control design. In contrast, many quadrupedal animals capable of agile running exhibit a pronounced anteriorly biased mass distribution, with the CoM located closer to the front of the body. This biological characteristic motivates a re-examination of whether a geometrically centered CoM necessarily corresponds to dynamically balanced loading between the fore- and hindlimbs during high-speed locomotion. To address this question, this study investigates the influence of longitudinal CoM position on load distribution during high-speed straight-line locomotion of quadruped robots. A unified analytical framework is established by combining whole-body force and pitch moment equilibrium, sagittal-plane kinematics, and Jacobian-based force-to-torque mapping, thereby linking longitudinal CoM position, foot-end support forces, and joint loads. Simulation validation is conducted on the Black Panther 2 quadruped robot using four central-body CoM configurations, denoted as ×0, ×5, ×10, and ×15. In the primary evaluation at 5 m/s, shifting the CoM forward from ×0 to ×15 reduces the absolute median fore–hindlimb differences in support force and joint torque by approximately 86.6% and 93.4%, respectively, indicating a transition from hindlimb-dominated loading toward cooperative load sharing between the fore and hindlimbs. Independent training runs with multiple random seeds further confirm the robustness of this load-redistribution trend to reinforcement learning variability. Consistent behavior is also observed at 8 m/s, while no evident degradation in turning response or locomotion stability is found under the tested turning and randomly generated rough-terrain conditions. These results demonstrate that a moderate forward shift of the longitudinal CoM can alleviate hindlimb load concentration and promote a more balanced fore–hindlimb load distribution, providing a theoretical basis for the morphological design and control optimization of high-speed quadruped robots.

High-speed human running data are difficult to acquire, limiting the availability of motion priors for high-speed humanoid running. To reduce dependence on motion capture and retargeting, this study proposes a framework that combines trajectory optimization with reinforcement learning. A planar multibody model generates reference motions across speeds subject to contact, periodicity, and actuator constraints. The optimized motions guide the training of a three-dimensional running policy through adversarial imitation and proximal policy optimization. Hardware experiments demonstrate treadmill running by the full-size BOLT humanoid at $10\mathrm{m}/\mathrm{s}$. In $2\mathrm{s}$ simulation windows under a $10\mathrm{m}/\mathrm{s}$ forward velocity command, comparison with a policy trained on retargeted human motions shows a more balanced distribution of joint loads relative to actuator capacity. The coefficient of variation of joint root-mean-square (RMS) utilization decreases from $0.397$ to $0.317$ across twelve joints, a reduction of $20.1\%$, with a corresponding reduction of $35.6\%$ across the six leg joints. These results support trajectory optimization as an effective source of motion priors for high-speed humanoid running and demonstrate a route from reference generation to hardware deployment without high-speed human motion data.

The inherent trade-off between structural compactness and functional versatility poses a fundamental challenge in biomimetic robotic hand design. This paper presents a multi-objective optimization framework for determining the optimal phalanx length allocation of a biomimetic finger under a fixed total-length constraint. Three performance criteria are formulated: grasp capability, measured by the area of a novel shared workspace (Region II) between power grasping and precision manipulation; kinematic dexterity, evaluated as the global average of the reciprocal condition number of the Jacobian matrix; and key-press range, defined as the maximum static fingertip span under perpendicularity and slope constraints. A full grid search reveals distinct optimal configurations for each objective. Pareto analysis of 117 non-dominated solutions shows that the key-press range is most sensitive to dimensional variations, with a 24.7% performance spread. A hierarchical selection strategy that prioritizes the key-press range while balancing the other two objectives yields a recommended compromise design. Experimental validation of the key-press reachability for the recommended compromise design achieves a 98.8% keystroke success rate over 1000 cross-row strikes without wrist movement. These results confirm its practical feasibility for fine manipulation tasks, while experimental characterization of the grasp capability and kinematic dexterity objectives remains as future work.

Robotic piano playing is a challenging benchmark for biomimetic dexterous manipulation, requiring precise timing, coordinated multi-finger motion, and stable key contact. This study proposes a robotic piano-playing framework based on a two-stage reinforcement learning curriculum. Musical Instrument Digital Interface (MIDI) data are converted into target-key and fingering grids to provide future musical goals for policy learning in a parallel MJLab simulation environment. A Soft Actor–Critic (SAC) agent takes a 2106-dimensional observation vector, including joint states, previous actions, musical phase, future key targets, fingering assignments, and piano-key states, and outputs a 21-dimensional continuous action vector for wrist, finger, and global hand-positioning control. Stage 1 weakens physical regularization to facilitate key-pressing acquisition, whereas Stage 2 strengthens power, velocity, acceleration, collision, posture, and finger-speed constraints to improve the regularity of policy outputs and readiness for real-world deployment. Simulation experiments on 30 s right-hand excerpts from Für Elise, Canon, and Beethoven’s Symphony No. 5 achieve frame-wise key-state F1 scores above 0.99 on the first two excerpts and approximately 0.945 on Beethoven. Real-world deployment uses open-loop playback of policy-generated high-level trajectories with low-level joint-position feedback and achieves F1 scores of 0.95, 0.91, and 0.83, respectively, while reproducing representative piano techniques such as chords, octaves, mixed black-and-white-key patterns, overlapping finger actions, and rapid sequential movements. These physical results demonstrate the feasibility of the proposed sim-to-real pipeline for complete 30 s executions; they are not intended as a statistical repeatability study. The results further show that biomimetic robotic hands can learn complex piano-playing skills from MIDI-based task objectives without relying on human motion demonstration trajectories.