Telemanipulation-based object-side picking with a suction gripper often faces challenges such as occlusion of the target object or the gripper and the need for precise alignment between the suction cup and the object’s surface. These issues can significantly affect task success rates in logistics environments. To address these problems, this study proposes a multi-mode hand gesture-based virtual reality (VR) locomotion method to enable intuitive and precise viewpoint control. The system utilizes a head-mounted display (HMD) camera to capture hand skeleton data, which a multi-layer perceptron (MLP) model processes. The model classifies gestures into three modes: translation, rotation, and fixed, corresponding to fist, pointing, and unknown gestures, respectively. Translation mode moves the viewpoint based on the wrist’s displacement, rotation mode adjusts the viewpoint’s angle based on the wrist’s angular displacement, and fixed mode stabilizes the viewpoint when gestures are ambiguous. A dataset of 4312 frames was used for training and validation, with 666 frames for testing. The MLP model achieved a classification accuracy of 98.4%, with precision, recall, and F1-score exceeding 0.98. These results demonstrate the system’s ability to address the challenges of telemanipulation tasks by enabling accurate gesture recognition and seamless mode transitions.

Soft pneumatic grippers are well-suited for grasping irregular objects owing to their inherent compliance and ability to adapt to a wide range of shapes and sizes. However, their ability to quantitatively estimate object size during the grasping process remains limited. To address this limitation, this study proposes a soft pneumatic gripper integrated with a piezoresistive CNT/PDMS composite sensor and investigates the feasibility of object size estimation using only sensor signals. The pressure-sensing characteristics of the CNT/PDMS sensor were evaluated over a pressure range of 0–500 kPa, and the 1 wt% CNT/PDMS sensor exhibited the highest sensitivity of approximately 0.016 kPa⁻¹ in the initial linear pressure region. To this end, the normalized resistance response under applied pneumatic pressure was analyzed independent of external visual information, and a size estimation method was established based on the relationship between initial contact pressure and object diameter. Grasping experiments using spherical objects of varying diameters revealed that the resistance response patterns were clearly distinguishable according to object size, with larger objects exhibiting significant resistance changes at lower applied pressures. These findings demonstrate the feasibility of estimating the size of a grasped object based on the grasp onset pressure derived from the sensor response. The results of this study provide a foundation for future soft robotic systems capable of recognizing contact conditions and object size through sensor-based feedback. Furthermore, these findings may be extended to adaptive manipulation technologies involving real-time pneumatic pressure control.

In industrial environments, providing intuitive spatial information via 3D maps is essential for maximizing the efficiency of teleoperation. However, existing SLAM algorithms generating 3D maps predominantly focus on improving robot localization accuracy, often neglecting the optimization of viewability required for human operators to clearly perceive object depth and structure in virtual environments. To address this, this study proposes a methodology to optimize the viewability of RTAB-Map-based 3D maps using the Taguchi method, aiming to enhance VR teleoperation efficiency and reduce cognitive workload. We identified eight key parameters that critically affect visual quality and utilized an L18 orthogonal array to derive an optimal combination that controls point cloud density and noise levels. Experimental results from a target object picking task demonstrated that the optimized 3D map reduced task completion time by approximately 9 s compared to the RGB image condition, achieving efficiency levels approaching those of the physical-world baseline. Furthermore, evaluations using NASA-TLX confirmed that intuitive visual feedback minimized situational awareness errors and substantially alleviated cognitive workload. This study suggests a new direction for constructing high-efficiency teleoperation interfaces from a Human–Robot Interaction perspective by expanding SLAM optimization criteria from geometric precision to user-centric visual quality.