Deep neural networks (DNNs) have shown an astonishing ability to unlock the complicated relationships among the inputs and their responses. Along with empirical successes, some approximation analysis of DNNs has also been provided to understand their generalization performance. However, the existing analysis depends heavily on the independently identically distribution (i.i.d.) assumption of observations, which may be too ideal and often violated in real-world applications. To relax the i.i.d. assumption, this article develops the covering number-based concentration estimation to establish generalization bounds of DNNs with $\tau $ -mixing samples, where the dependency between samples is much general including $\alpha $ -mixing process as a special case. By assigning a specific parameter value to the $\tau $ -mixing process, our results are consistent with the existing convergence analysis under the i.i.d. case. Experiments on simulated data validate the theoretical findings.

During postharvest storage, fruit firmness usually decreases due to respiration and bruise, the former of which indicates the fruit ripeness while the latter negatively influence consumers' taste preference. This paper presents a portable and low-cost device using vision-based tactile information to evaluate fruit firmness in a non-destructive manner. The device consists of a camera, LED lights, and a soft sensing layer with small bumps to capture detailed tactile information of the fruit. Two working modes are designed and a CNN-LSTM architecture is developed to relate the tactile information to fruit overall firmness or detect local firmness distortion. According to the experimental results, an R2 up to 92.9% was achieved for the evaluation of the overall firmness of Cuixiang kiwifruit, and accuracy of 98.0% was obtained for the detection of local firmness distortion of Fuji apples. These results demonstrate the efficacy of the proposed solution to evaluate fruit firmness featuring high precision, and its non-destructive and potable nature is also anticipated to be favorable by the fresh fruit market.

This paper extends our previous work on a pneumatic bending module and presents two more modules for rotational and translational motions. In these modules, antagonistic chambers enveloped by rigid shells are adopted to realize bidirectional actuation, and they are characterized by safe actuation, enhanced torque/force output, independent stiffness tuning, and real-time position control. Due to their mechanical modularity, they can be conveniently assembled into robotic systems with multiple degrees of freedom (DoFs) according to different requirements. A complete workflow is presented including the module design, fabrication, theoretical modelling, controller design, and experimental validation. A reconfigurable robotic arm with high dexterity is also assembled using these modules, demonstrating the effectiveness of the proposed modules to develop robotic systems for safe, forceful, and precise tasks.

This paper describes a new type of bending module inspired, in part, by the musculoskeletal structure of the lobster leg joint. The bending module proposed combines enhanced torque output, reconfigurability in assembling, safe compliant actuation, and accurate control on its mechanical performance. In this module, antagonistic soft chambers are enveloped by exoskeleton shells, and the bending angle and the stiffness can be independently adjusted by controlling the input pressure in the two chambers. Theoretical models are developed to characterize the relationships between the input pressure, bending angle, and stiffness, and a controller for angle control and stiffness tuning is constructed with experimental validation. The fabricated module can reach the maximum torque output of 109.7 N•mm under 40 kPa and the stiffness range from 40 to 220 N•mm / rad, demonstrating its capacity to fulfill both safe interactions and forceful tasks.

Classical rigid-bodied robotic systems are presented with proven success in theoretical development and industrial applications, are recently challenged by the emergence of soft robotics due to a growing need in physical human-robot interactions (pHRI), such as wearable devices, medical robots, personal robots, etc. In this paper, we present the design and fabrication of a robust, hybrid bending actuator build from both rigid and soft components inspired by crustaceans, where its bending radius and axis can be mechanically programmed through the selective activation of the rigid exterior joints, actuated by the soft actuators inside. The hybrid actuator was experimentally measured in terms of bending and force tests to demonstrate the utility of this design. Finally, a case study was presented to demonstrate its capacity to adapt to specific objects geometry, anticipating its potential application in situations where compliance is the priority.

This paper presents preliminary results of the design, development, and evaluation of a hand rehabilitation glove fabricated using lobster-inspired hybrid design with rigid and soft components for actuation. Inspired by the bending abdomen of lobsters, hybrid actuators are built with serially jointed rigid shells actuated by pressurized soft chambers inside to generate bending motions. Such bio-inspiration absorbs features from the classical rigid-bodied robotics with preciselydefined motion generation, as well as the emerging soft robotics with light-weight, physically safe, and adaptive actuation. The fabrication procedure is described, followed by experiments to mechanically characterize these actuators. Finally, an open-palm glove design integrated with these hybrid actuators are presented for a qualitative case study. A hand rehabilitation system is developed by learning patterns of the sEMG signals from the users forearm to train the assistive glove for hand rehabilitation exercises.