This paper presents the design and experimental validation of a torque-controlled robotic wrist for peg-in-hole tasks. The proposed wrist features a serial pitch-yaw joint configuration that enhances dexterity while maintaining compactness. The design integrates stepper motors, harmonic geartrains, and compliant mechanisms to optimize torque output and control accuracy. A compliant pulley and a compliant cap are introduced, enabling embedded torque sensing without the need for external sensors, thereby reducing system complexity and improving response time. Experimental results demonstrate the effectiveness of the wrist in torque accuracy and misalignment correction during peg-in-hole assembly, highlighting the benefits of the compliant-driven torque sensing approach. Compared to existing robotic wrists, the proposed design achieves a higher torque density. The findings contribute to advancing robotic wrist technology, particularly in applications requiring precise force modulation, high dexterity, and adaptable compliance.

This paper proposes efficient trimmed-sample Monte Carlo (TSMC) methodology and novel yield-aware design flow for analog circuits. This approach focuses on "trimming simulation samples" to speedup MC analysis. The best possible yield and the worst performance are provided "before" MC simulations such that designers can stop MC analysis and start improving circuits earlier. Moreover, this work can combine with variance reduction techniques or low discrepancy sequences to reduce the MC simulation cost further. Using Latin Hypercube Sampling as an example, this approach gives 29x to 54x speedup over traditional MC analysis and the yield estimation errors are all smaller than 1%. For analog system designs, the proposed flow is still efficient for high-level MC analysis, as demonstrated by a PLL system.