Caterpillar-type robots are widely used for medium- and large-sized pipe inspections. However, existing prototypes smaller than 80 mm lack both an active variable diameter capability and a contact force sensing function, which are crucial for safe and automatic exploration of unknown rigid/soft pipes (e.g., the colon). This study develops a variable diameter caterpillar-type miniature robot (VCMR) featuring a small size of Φ34.6 mm × 41 mm, a large variable-diameter range of 34.6-89.6 mm, and an integrated contact force sensing function. The VCMR actively adapts to pipe diameter changes using contact force feedback, demonstrates high load capacity in both vertical and horizontal rigid/soft pipes, and traverses a 150-cm colon phantom with sharp bends at an average velocity of 3.07 ± 0.48 cm/s. It holds promise for exploring tiny variable-diameter rigid/soft pipes and delivering cargoes through such pipes.

Grasping unknown objects from a single view has remained a challenging topic in robotics due to the uncertainty of partial observation. Recent advances in large-scale models have led to benchmark solutions such as GraspNet-1Billion. However, such learning-based approaches still face a critical limitation in performance robustness for their sensitivity to sensing noise and environmental changes. To address this bottleneck in achieving highly generalized grasping, we abandon the traditional learning framework and introduce a new perspective: similarity matching, where similar known objects are utilized to guide the grasping of unknown target objects. We newly propose a method that robustly achieves unknown-object grasping from a single viewpoint through three key steps: 1) Leverage the visual features of the observed object to perform similarity matching with an existing database containing various object models, identifying potential candidates with high similarity; 2) Use the candidate models with pre-existing grasping knowledge to plan imitative grasps for the unknown target object; 3) Optimize the grasp quality through a local fine-tuning process. To address the uncertainty caused by partial and noisy observation, we propose a multi-level similarity matching framework that integrates semantic, geometric, and dimensional features for comprehensive evaluation. Especially, we introduce a novel point cloud geometric descriptor, the C-FPFH descriptor, which facilitates accurate similarity assessment between partial point clouds of observed objects and complete point clouds of database models. In addition, we incorporate the use of large language models, introduce the semi-oriented bounding box, and develop a novel point cloud registration approach based on plane detection to enhance matching accuracy under single-view conditions. Real-world experiments demonstrate that our proposed method significantly outperforms existing benchmarks in grasping a wide variety of unknown objects in both isolated and cluttered scenarios, showcasing exceptional robustness across varying object types and operating environments.

This paper presented a regrasp planning method to eliminate grasp uncertainty while considering the geometric constraints of a fixture. The method automatically finds the Stable Placement Poses (SPPs) of an object on a Triangular Corner Fixture (TCF), elevates the object from its SPPs to dropping poses and finds the Deterministic Dropping Poses (DDPs), builds regrasp graphs by using the SPP-DDP pairs and their associated grasp configurations, and searches the graph to find regrasp motion sequences for precise assembly. Since the SPPs and their associated regrasps are constrained by the TCF’s geometry and have high precision, the final object poses regrasped via it has low uncertainty and can be directly used for assembly by position control. In the experimental section, we study the performance of analytical and learning-based methods for estimating the DDPs of different objects and quantitatively examine the proposed method’s ability to suppress uncertainty using assembly tasks like peg-in-hole insertion and sheathing tubes, aligning holes, mounting bearing housings, etc. The results demonstrate the method’s robustness and efficacy. Note to Practitioners—In production lines, robots interact with peripheral devices to improve efficiency and reduce uncertainty. In this work, we focus on a particular peripheral device – a Triangular Corner Fixture (TCF) made by three inclined and mutually perpendicular plates. We study using the TCF to improve manipulation precision. The inclined plates of the TCF form a gravity bucket that holds dropped objects in stable states under gravity. In a real scenario, a robot picks up an object and releases it above the TCF. The released object will reach a stable state on the TCF. Then, the robot regrasps and moves the stabilized object to the target pose with reduced uncertainty. Using the method proposed in this paper, a robot can automatically finish the above procedure by finding all the object’s stable states in the TCF, planning grasp configurations, invalidating infeasible states and grasps, building regrasp graphs and searching the graph to find a regrasp motion sequence that moves the object to a goal pose with high precision for assembly. In industrial applications, the proposed method has the potential to improve the flexibility of robotic systems for high-precision tasks. In the research fields, it may promote the research on sensorless manipulation and extrinsic manipulation, and push forward the studies in robotic regrasp.

This paper presents a stiffness-changeable soft finger using chain mail jamming. This finger can achieve adaptive grasping and in-hand manipulation by reshaping and exerting changeable gripping force. The jamming phenomenon happens when particles in a chamber get interlocked where confining pressure is exerted at their boundaries, which is widely used to construct mechanisms with changeable stiffness. Compared with the traditional granular media, chain mail has a lower packing fraction and provides a stronger tensile force. In this paper, we proposed to apply chain mail jamming to the field of robotic finger design. Especially, we propose the design of the finger, the fabrication process, the method of predicting gripping force, and the grasping strategies. The experiments quantitatively verify the model of gripping force prediction. The demonstrations validate the advantages of adaptive grasp by picking a variety of items including foods, goods, and industrial components, and show the application of in-hand manipulation.

This article develops a mechanical screwing tool and its manipulation policies for two-finger parallel robotic grippers. The tool is based on a combined scissor-like element (SLE) and double-ratchet mechanism that converts the gripping motion of two-finger parallel grippers into a continuous rotation to realize tasks like fastening screws. The tool is entirely mechanical. There is no need for external cable connections. The manuscript includes two parts. For one thing, it shows the details of the tool design, optimizes the tool’s dimensions and effective stroke lengths, and studies the contacts and forces to achieve stable grasping and screwing. For another, it presents the related manipulation and control policies, including recognizing the tool, changing tool poses, and completing screw fastening tasks. The designed tool, together with the related manipulation and control policies, are analyzed and verified in several real-world applications. The results show that the tool has satisfying mechanical properties. Robots with parallel grippers can robustly and flexibly use the tool to fasten screws. The tool can also be used collaboratively with other tools to finish difficult tasks. In the future, similar tools are expected to replace special-purpose end-effectors or tool changers for more flexible robot integration.