This paper proposes a fair control framework for multi-robot systems, which integrates the newly introduced Alternative Authority Control (AAC) and Flexible Control Barrier Function (F-CBF). Control authority refers to a single robot which can plan its trajectory while considering others as moving obstacles, meaning the other robots do not have authority to plan their own paths. The AAC method dynamically distributes the control authority, enabling fair and coordinated movement across the system. This approach significantly improves computational efficiency, scalability, and robustness in complex environments. The proposed F-CBF extends traditional CBFs by incorporating obstacle shape, velocity, and orientation. FCBF enhances safety by accurate dynamic obstacle avoidance. The framework is validated through simulations in multi-robot scenarios, demonstrating its safety, robustness and computational efficiency.

Anthropomorphic robotic arms, mimicking the structure and function of human arms, show great potential for helping people in various tedious and repetitive household tasks. However, such arms mostly consist of multiple serial links controlled independently by actuators at joints with high reduction ratios, posing challenges in household services in terms of load capacity, responsiveness, and safety. In this paper, we propose a high-performance anthropomorphic arm called TRX-Arm based on differential cable transmission, characterized by features of high dynamics, high load capacity, and inherent compliance. TRX-Arm is composed of three deferential cable-driven coupling joints and one independent roll joint. Thanks to the cable differential transmission, the joints are capable of achieving doubled torque and stiffness without replacing motors. To enhance safety in human-robot interaction, the actuators including motors, reducer, belt, and pulley are mounted at the shoulder near the base and drive the joints remotely using cables, thereby minimizing the inertia of the whole arm. The workspace of TRX-Arm has a volume of 1.56 m3, much larger than that of the human arm. Real experiments show its capabilities including high repeatability and load capacity as well as high dynamic behavior of a dual-arm robot platform built with TRX-Arms.

Objects of daily life are designed to suit the human hand. Without major modifications to these objects and our environments, robots will need end-effectors with human hand-like configuration and dexterity to efficiently operate on them. Tight integration of tactile and proprioceptive sensors are also critical to ensure robust execution of manipulation policies without sacrificing range-of-motion. Reliability is also key, and a mechanically robust, easy to repair end-effector is important to minimize downtime. To meet these challenges, we designed a 13 degree-of-freedom anthropomorphic hand with over 1000 tactile sensing elements, named TRX-Hand5. Also embedded within are positional encoders and cable tension sensors to provide proprioceptive perception. TRX-Hand5 has a novel biomimetic topology with six small posture motors in the palm to replicate the function of intrinsic hand muscles and five large power motors in the forearm to play the role of forearm flexor muscles. The whole hand weighs 2.6 kg with its dimensions comparable to those of an adult male’s hand and is capable of actuating its fingertips at over 200°/s while exerting up to 22 N of force. The system can be disassembled in modules for easy maintenance.

This research presents a novel Collective Robotic Construction (CRC) system named RECCraft. The RECCraft hardware system is composed of the mobile manipulation vehicles, the cubic blocks, and the folding ramp blocks. Solid connection and easy removal of the blocks are achieved by an electropermanent magnet and silicon steel sheets. With one degree of freedom (DOF) lifting manipulator, the robot can carry a block 3.7 times its volume. An active folding ramp block can provide a robust passage to the upper level for the robot. Our study focuses on systemic improvement of the construction speed and reliability of the robotic construction system. Visual perception system realized by Apritag is adopted, featured by convenient deployment and high precision, to provide a reliable guarantee for robotic construction. RL-based planner provides end-to-end solution for planning tasks of building multi-layer constructions, which is validated by simulation platform and real prototype. Compared with construction speed of existing robotic construction systems, our proposed RECCraft system achieves state-of-the-art level. The robot builds a 2-layer construction by RL-based planner in 4 minutes and 16 seconds, which achieves construction volumetric throughput of 6.7×105mm3/s.

Universal grasping for a diverse range of objects is a challenging problem in robotics, especially in the presence of mixed properties with fragile/rigid and heavy/light. Toward universal grasping, this article presents a practical and systematic grasping control framework that enables a variable stiffness gripper to handle the objects with diverse properties using a category-aware force regulation approach, termed classification-based force grasping. Under this framework, a convolutional neural network (CNN) is employed to classify the category of the grasping object, and a grasping force is determined based on the classified category through a database that records a predefined force magnitude per category. Sequentially, the gripper can be adjusted to a force-optimized stiffness, which facilitates the achievement of an accurate grasping force regulation in a large range. Technically, two novel enabling modules are developed for grasping classification and execution, respectively. First, a novel weight imprinting technique based on center-guided feature embedding is proposed for object classification. It enables the CNN to efficiently handle novel object categories using only a few samples even without retraining/fine-tuning. Second, a vision-based grasping force sensing module is developed, which takes advantage of the specifically designed variable-stiffness gripper. Its grasping force can be estimated from the deflection angle of finger flexure by the vision so that the contact force can be sensed and regulated. Remarkably, only single-source vision information is needed for both of the above modules without any additional force sensor. Experiments are conducted extensively to evaluate the performance of the proposed force grasping approach.Note to Practitioners—Robotic grasping often needs to handle novel categories of objects. As a result, frequent retraining of the classification neural network is a pain point, which is tedious and prone to overfitting with only a few samples. In this work, metric learning is introduced for grasping classification where a novel kind of weight imprinting classification is proposed to handle the novel classes by better feature embedding and directly setting the classifier weights without retraining or fine-tuning. Together with the benefits from the variable stiffness feature of the gripper, the proposed vision-based force grasping approach can handle a wide range of objects from fragile to heavy, and the grasping force is controllable from 0.2 N onward to the motor limitation. The controllable grasping force resolution of the proposed vision grasping is better than 0.05 N, the accuracy of the grasping force is evaluated from 0.2 to 12 N, and the evaluated grasping objects are from extremely fragile potato chips and eggshell to heavy flange and metal block.

Robot hand is essential for a fully functional robot and designing a good robot hand is a sophisticated job that challenges the designer’s knowledge and experience. This paper presents a computational framework for automatic optimal robot hand design based on reinforcement learning (RL), which considers desired grasping tasks, grasp control strategies, and performance quality measures altogether. The RL-based framework intends to grow finger joints with different types and link lengths at different positions from null. Then, the reward function for such a growing action is defined in terms of quality indexes of the generated robot hand to perform desired grasping tasks under expected control strategies. To demonstrate the effectiveness of this framework, in this paper we set the desired task to simply grasping objects of three primitive shapes (i.e., box, cylinder, and sphere) with predefined hand positions and strategies to close fingers to achieve grasps for each object. The force closure condition, quantitative stability indexes, and energy consumption of grasps as well as some penalty terms are used to assemble the reward function. Through simulation and practical prototype experiments, we show that capable robot hands can be automatically generated by the proposed framework. Potential factors that affect the output of the framework and deserve further exploration are also discussed.

Having the merits of chemical inertness and immunity to electromagnetic interference, light weight, small size, and softness, optical waveguides have attracted much attention in making tactile sensors recently. This paper presents a new design of waveguide using two layers of cores, one of which has an uniform width and the other has an incremental width. It is deduced and verified that the contact force can be derived from the light power loss in the uniform-width core, while the contact position can be derived from the light power loss in the other core together with the estimated force. By this dual-core design, a single waveguide can simultaneously and continuously measure the contact force and position along it, which makes it very suited for integration on some thin long robotic parts, such as robotic fingers. A hardware experiment has been conducted to demonstrate its effectiveness on a two-finger gripper in an assembly task. The dual-core waveguide achieves 2 mm spatial resolution and 0.1 N sensitivity.

This paper presents a novel structure-controlled variable stiffness robotic gripper that enables adaptive gripping of soft and rigid objects with a wide range of compliance. With the structure-controllable principle, the stiffness is controlled by the mechanical structure configurations rather than by material properties or electronic means. The principle is realized by changing the effective second moment of area of the gripper finger through rotating a built-in flexure hinge shaft. Based on this principle, the states of the stiffness can be continuously, instead of discretely, studied and assessed over the intermediate states from compliant to almost completely rigid. A variable stiffness mechanism has been developed to demonstrate the validity of the proposed principle. It enables that the finger stiffness and gripping position are independently controlled. With the introduction of flexure hinges, the undesired lateral buckling resulted from the rotation of a normal leaf spring is eliminated. In addition, a two-finger parallel gripper with this variable stiffness mechanism is developed which can provide the grasping stiffness according to the grasping task requirements. The effectiveness of the gripper has been demonstrated to handle the objects range from light, fragile to heavy, rigid without using any feedback loop or soft pads.

This paper presents a new variable stiffness actuator (VSA) based on a structure-controlled method: controlling the mechanical structure of the actuator by rotating four flexure hinges. The VSA possesses a property that the output position and stiffness are independently controlled. This is realized by a serial arrangement of a principle driving motor, a small stiffness variation motor and a novel variable stiffness mechanism (VSM). The VSM consists of four combined notch flexure hinge frames, each of which contains an inner rotary flexure shaft and outer stationary hinge housing. The stiffness is adjusted by rotating the inner flexure shaft to change the second moment of area. Its value is independent to the VSA output angle when the flexure rotational angle is fixed. The working principle of this VSA is elaborated and the optimization mechanical design of the VSM is presented. A prototype has been implemented and its primary characteristic test results validate the design features.

This paper presents a new flexible fixtureless assembly (FFA) approach for building T-joint structures automatically. The method is based on a Fixturing Assistive Gripper (FAG) mounted on a robot arm for forming T-joint sections. It eliminates possible uncertainties in terms of positioning and size errors during fit-up process. The method is verified by a robotic assembly workcell which includes dual robots and a simple machine vision unit. The basic algorithms for image processing, task planning, dual robot cooperative motion and path planning are developed for assembling a simple structure which predominantly formed by T-joint sections. Experiments show that the workcell can effectively eliminate the alignment error and successfully complete T-joint assembly task without the complicated sensing system typically required in the existing approaches.