Light Detection and Ranging (LiDAR)–Inertial Odometry (LIO), which tightly fuses complementary data from LiDAR and Inertial Measurement Units (IMUs), is a key technology for high-precision state estimation in legged robot navigation. However, conventional Iterative Closest Point (ICP)-based LIO  frameworks provide only pose constraints. Their position estimates often exhibit centimetre-level jitter due to LiDAR measurement noise, especially when the robot is stationary or moving slowly. This temporal inconsistency degrades the performance of downstream perception-driven motion planning and control.  In this paper, we propose LiDAR–Inertial–Joint Odometry (LIJO), a novel state estimation framework for quadruped robots that integrates LiDAR, IMU, and joint encoder measurements within a manifold extended Kalman filter (EKF). The torso velocity is first estimated from joint angles and angular velocities via forward kinematics and is then used as a proprioceptive velocity factor in the EKF prediction step.  To cope with foot slippage and aggressive motions, we further design a dynamic weighting scheme that adaptively adjusts the confidence of the joint-based velocity factor according to the current motion speed,  while treating IMU measurements as filter observations rather than inputs. The resulting system maintains the localization accuracy of state-of-the-art LIO methods and significantly suppresses high-frequency jitter in the estimated trajectory. Extensive experiments on a real quadruped robot in challenging outdoor scenarios, including steep staircases and large-scale loop trajectories, show that LIJO produces smoother and more stable odometry than existing LIO and kinematic-inertial baselines, while preserving real-time performance. The proposed approach thus provides more reliable state inputs for perception and control modules in all-terrain legged locomotion.

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.