arXiv subjects

Hoang Hiep Ly

Publications and source records attributed to Hoang Hiep Ly.

5 recordsLinked to original sources

arXiv2026

Probe-to-Grasp Manipulation Using Self-Sensing Pneumatic Variable-Stiffness Joints

Grasping deformable objects with varying stiffness remains a significant challenge in robotics. Estimating the local stiffness of a target object is important for determining an optimal grasp pose that enables stable pickup without damaging the object. This paper presents a probe-to-grasp manipulation framework for estimating the relative stiffness of objects using a passive soft-rigid two-finger hybrid gripper equipped with self-sensing pneumatic variable-stiffness joints. Each finger of the gripper consists of two rigid links connected by a soft pneumatic ring placed at the joint, enabling both compliant interaction and controllable joint stiffness via internal pressurization. By measuring the pressure inside the pneumatic ring, we can estimate the interaction force during contact. Building on this, we propose a practical probing strategy to infer relative object stiffness by correlating the estimated normal force with known gripper closing displacement. We validate the self-sensing model through stiffness characterization experiments across bending angles and pressure ranges, and demonstrate stiffness-aware probing-and-grasping in real-life applications: selecting grasp locations on fruits with spatially varying stiffness. The proposed system offers a minimal, low-cost sensing approach for stiffness-aware soft manipulation while retaining probing and grasping capability.

cs.RO↗

arXiv2026

MobiDock: Design and Control of A Modular Self Reconfigurable Bimanual Mobile Manipulator via Robotic Docking

Multi-robot systems, particularly mobile manipulators, face challenges in control coordination and dynamic stability when working together. To address this issue, this study proposes MobiDock, a modular self-reconfigurable mobile manipulator system that allows two independent robots to physically connect and form a unified mobile bimanual platform. This process helps transform a complex multi-robot control problem into the management of a simpler, single system. The system utilizes an autonomous docking strategy based on computer vision with AprilTag markers and a new threaded screw-lock mechanism. Experimental results show that the docked configuration demonstrates better performance in dynamic stability and operational efficiency compared to two independently cooperating robots. Specifically, the unified system has lower Root Mean Square (RMS) Acceleration and Jerk values, higher angular precision, and completes tasks significantly faster. These findings confirm that physical reconfiguration is a powerful design principle that simplifies cooperative control, improving stability and performance for complex tasks in real-world environments.

cs.RO↗

arXiv2026

Soft Rigid Hybrid Gripper with Inflatable Silicone Pockets for Tunable Frictional Grasping

Grasping objects with diverse mechanical properties, such as heavy, slippery, or fragile items, remains a significant challenge in robotics. Conventional rigid grippers typically rely on increasing the normal forces to secure an object, however, this can cause damage to fragile objects due to excessive force. To address this limitation, we propose a soft rigid hybrid gripper finger that combines rigid structural shells with soft, inflatable silicone pockets, which could be integrated into a conventional gripper. The hybrid gripper can actively modulate its surface friction by varying the internal air pressure of the silicone pockets, enabling the gripper to securely grasp objects without increasing the gripping force. This is demonstrated by fundamental experimental results, in which an increase in internal pressure leads to a proportional increase in the effective coefficient of friction. The gripping experiments also show that the integrated gripper can stably lift heavy and slippery objects or fragile, deformable objects, such as eggs, tofu, fruits, and paper cups, with minimal damage by increasing friction rather than applying high force.

cs.RO↗

arXiv2025

Hybrid Gripper Finger Enabling In-Grasp Friction Modulation Using Inflatable Silicone Pockets

Grasping objects with diverse mechanical properties, such as heavy, slippery, or fragile items, remains a significant challenge in robotics. Conventional grippers often rely on applying high normal forces, which can cause damage to objects. To address this limitation, we present a hybrid gripper finger that combines a rigid structural shell with a soft, inflatable silicone pocket. The gripper finger can actively modulate its surface friction by controlling the internal air pressure of the silicone pocket. Results from fundamental experiments indicate that increasing the internal pressure results in a proportional increase in the effective coefficient of friction. This enables the gripper to stably lift heavy and slippery objects without increasing the gripping force and to handle fragile or deformable objects, such as eggs, fruits, and paper cups, with minimal damage by increasing friction rather than applying excessive force. The experimental results demonstrate that the hybrid gripper finger with adaptable friction provides a robust and safer alternative to relying solely on high normal forces, thereby enhancing the gripper flexibility in handling delicate, fragile, and diverse objects.

cs.RO↗

arXiv2025

Enhancing Rotated Object Detection via Anisotropic Gaussian Bounding Box and Bhattacharyya Distance

Detecting rotated objects accurately and efficiently is a significant challenge in computer vision, particularly in applications such as aerial imagery, remote sensing, and autonomous driving. Although traditional object detection frameworks are effective for axis-aligned objects, they often underperform in scenarios involving rotated objects due to their limitations in capturing orientation variations. This paper introduces an improved loss function aimed at enhancing detection accuracy and robustness by leveraging the Gaussian bounding box representation and Bhattacharyya distance. In addition, we advocate for the use of an anisotropic Gaussian representation to address the issues associated with isotropic variance in square-like objects. Our proposed method addresses these challenges by incorporating a rotation-invariant loss function that effectively captures the geometric properties of rotated objects. We integrate this proposed loss function into state-of-the-art deep learning-based rotated object detection detectors, and extensive experiments demonstrated significant improvements in mean Average Precision metrics compared to existing methods. The results highlight the potential of our approach to establish new benchmark in rotated object detection, with implications for a wide range of applications requiring precise and reliable object localization irrespective of orientation.

cs.CV↗