Recent advances in legged robots have substantially improved their locomotion capabilities over outdoor and complex terrains. However, quiet locomotion for humanoid robots in noise-sensitive indoor environments remains underexplored, despite its growing importance in human-centered applications.  While encouraging progress has been made in quadrupedal robots, transferring the quiet locomotion ability to humanoid robots remains nontrivial due to their fundamentally different foot-ground contact patterns. This paper proposes a control method for reducing foot–ground contact noise during humanoid walking, achieving compliant contact and continuous regulation of locomotion noise by establishing a foot corner contact model along with a virtual compliance parameter. The experimental results show that the average sound pressure level is reduced by 4.88 dB.

Soft grippers utilize compliant materials to achieve adaptable grasping, yet they often face challenges in accommodating objects with widely varying dimensions due to their fixed kinematic structures. This paper presents the design, fabrication, modeling, and control of a novel soft gripper featuring a rigid-flexible coupled variable range adapter. By integrating a motorized crank-slider mechanism with soft pneumatic fingers, the gripper achieves a dynamic volumetric workspace capable of manipulating objects ranging from compact to large geometries. Theoretical modeling and experimental characterization reveal that the adapter serves a dual purpose: it not only expands the effective workspace but also functions as a mechanical force amplifier, capable of exponentially boosting the contact force through kinematic reconfiguration. Furthermore, an intuitive Human-in-the-Loop (HITL) teleoperation strategy is established using wearable flex sensors. This control framework maps human gestures to robotic actuation, leveraging human visual feedback as a high-level perception loop to validate the open-loop response of the soft actuators. Experimental results demonstrate that this integrated system significantly improves adaptability and payload stability for diverse object geometries compared to fixed-base counterparts.

Achieving autonomous navigation requires Maritime Autonomous Surface Ships (MASS) to overcome difficulties in recognizing intricate multi-ship encounter situations and developing appropriate collision avoidance strategies. To address the above issues, this study first integrates the Velocity Obstacle (VO) with Closest Point of Approach/Time to Closest Point of Approach (CPA/TCPA) into the collision risk identification of ships. This enables the own ship (OS) to determine the risk posed by target ships (TSs) in the scenario. Secondly, the paper further discusses complex multi-ship encounter scenarios, based on 1972 International Regulations for Preventing Collision at Sea (COLREGs), it classifies responsibilities to determine the set of give-way ship and stand-on ship obligations. Finally, the derived responsibility set is incorporated into Dynamic Window Approach (DWA), allowing the enhanced Multi-Vessels Velocity Obstacle and Improved Dynamic Window Approach (MVO-IDWA) algorithm to automatically select optimal decisions in complex multi-ship encounter scenarios and ensure the ship reaches its destination safely. Centering on the challenge of avoiding collision decision in multi-ship complex encounter scenarios, this paper proposes the integration of ship responsibility sets with the MVO-IDWA algorithm. Analysis of the results establishes that the proposed algorithm can consider the inter-ship responsibilities, risk levels, and movement trends of TSs in multi-ship encounters, thereby realizing autonomous ship collision avoidance.