Current soft grippers lack active variable stiffness composite fingers and flexible modular configuration, which limit their robust grasp abilities. We proposed a morphology configurable soft gripper (MCSG) assembled with self-adaption modular composite fingers (SAMCFs), whose grasping force can be up to 193 N. The SAMCF with three actuator modules embedded arranged sponge and paperboard and one sensor module made with preinflated air chamber is developed to perform soft grasp with high load capacity and self-sensing ability. Through assembling corresponding SAMCFs to main frame, MCSGs with dual fingers, with three fingers, with four fingers, and with six fingers are manufactured to adapt for grasping objects with different shapes, sizes, and weights. A pneumatic control system with hybrid valve combination is designed to output air pressure from negative to positive to perform contraction and elongation motion simultaneously, and output positive pressure to provide preset pressure for sensor modules. Finally, a multichannel pneumatic control system is developed to control MCSGs to grasp a variety of objects, such as cherry tomato (10 g), egg (170 g), banana (155 g), and stool (3570 g). Furthermore, MCSGs can complete complicated tasks in daily life, such as water decanting, bottle cap unscrewing, mobile phone charging and water pumping.

The utilization of robots in computer, communication, and consumer electronics (3C) assembly has the potential to significantly reduce labor costs and enhance assembly efficiency. However, many typical scenarios in 3C assembly, such as the assembly of flexible printed circuits (FPCs), involve complex manipulations with long-horizon steps and high-precision requirements that cannot be effectively accomplished through manual programming or conventional skill-learning methods. To address this challenge, this article proposes a learning-based framework for the acquisition of complex 3C assembly skills assisted by a multimodal digital-twin environment. First, we construct a fully equivalent digital-twin environment based on the real-world counterpart, equipped with visual, tactile force, and proprioception information, and then collect multimodal demonstration data using virtual reality (VR) devices. Next, we construct a skill knowledge base through multimodal skill parsing of demonstration data, resulting in primitive policy sequences for achieving 3C assembly tasks. Finally, we train primitive policies via a combination of curriculum learning, residual reinforcement learning, and domain randomization methods and transfer the learned skill from the digital-twin environment to the real-world environment. The experiments are conducted to verify the effectiveness of our proposed method.

Autonomous indoor service robots are affected by multiple factors when they are directly involved in manipulation tasks in daily life, such as scenes, objects, and actions. It is of self-evident importance to properly parse these factors and interpret intentions according to human cognition and semantics. In this study, the design of a semantic representation framework based on a knowledge graph is presented, including (1) a multi-layer knowledge-representation model, (2) a multi-module knowledge-representation system, and (3) a method to extract manipulation knowledge from multiple sources of information. Moreover, with the aim of generating semantic representations of entities and relations in the knowledge base, a knowledge-graph-embedding method based on graph convolutional neural networks is proposed in order to provide high-precision predictions of factors in manipulation tasks. Through the prediction of action sequences via this embedding method, robots in real-world environments can be effectively guided by the knowledge framework to complete task planning and object-oriented transfer.

Compared with traditional rigid gripper with joint-linkage structure, novel soft robotic gripper gives rise to continuous concern for the advantages of no-damage grasping, convenient manufacture, easy control, and low cost. In this study, we design and built two kinds of soft robotic grippers with four fiber-reinforced soft actuators which are distributed in circular and rectangle shapes for single and twin contacts grasping. A novel hybrid valve pneumatic control scheme combining proportional and solenoid valves is proposed. Also, a mode controllable hybrid valve pressure control method is proposed to adjust internal pressure of soft robotic grippers to adapt to different grasping tasks. The experiment results verify that the performances of hybrid valve outperform those of individual proportional valve or solenoid valve in the aspects of response time and steady-state accuracy. The hybrid valve has wide range of pressure regulation, result in that the soft robotic grippers are qualified to grasp various objects with different shapes, sizes, and weights.

Existing soft hand mostly focus on mechanism and material innovation, as well as biological behavior imitation, which have problems of fewer sensing abilities and unfriendly human-robot interaction (HRI). In this paper, we propose a novel bionic soft hand (BSH) with dexterity operation, multi-modal perception and bidirectional HRI. For dexterity operation, we first designed and fabricated a dual-joint bellows (DJB) soft finger, and then, five fingers were integrated into BSH with inertial measurement units (IMUs), contact pressure sensors, curvature sensors and air pressure sensors distributed appropriately. For multi-modal perception, the control and perception system were established to drive BSH in pneumatic mode and process the multi-modal sensor information to perform robust perception capabilities using support vector machine (SVM), extreme learning machine (ELM) and BP neural network (BPNN). For bidirectional HRI, a data glove with force reproduction was utilized to perform posture mapping and tactile force mapping. The experimental results verify that the proposed BSH has a wide workspace range and dexterous operation ability, and can accurately recognize human posture and object properties. Also, hand posture mapping and tactile force mapping have been demonstrated Note to Practitioners —This work was motivated by the research status of existing soft hands that lack sensing abilities and friendly HRI, and aimed to develop a bionic soft hand (BSH) with dexterity operation, multi-modal perception and tactile force interaction. We increased the degrees of freedom of BSH by adopting a bellows structure to build a two-joint soft finger, enhanced the perception ability of BSH by utilizing multi-modal sensors and multiple machine learning algorithms, performed posture mapping and tactile force mapping for bidirectional HRI by designing a novel data glove with multi-modal sensors and a variable stiffness jamming layer. Experimental results verify that the proposed BSH has a wide workspace range and dexterous operation ability, and can accurately recognize human posture and object properties by multi-modal sensors. Future work will focus on imitating the joint structure of human hand and make BSH be closer to the dexterity and perception performance of human hand. Also introducing humanbrain-machine interface is a meaningful challenge.