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Current robotic studies are focused on the performance of specific tasks. However, such tasks cannot be generalized, and some special tasks, such as compliant and precise manipulation, fast and flexible response, and deep collaboration between humans and robots, cannot be realized. Brain-inspired intelligent robots imitate humans and animals, from inner mechanisms to external structures, through an integration of visual cognition, decision making, motion control, and musculoskeletal systems. This kind of robot is more likely to realize the functions that current robots cannot realize and become human friends. With the focus on the development of brain-inspired intelligent robots, this article reviews cutting-edge research in the areas of brain-inspired visual cognition, decision making, musculoskeletal robots, motion control, and their integration. It aims to provide greater insight into brain-inspired intelligent robots and attracts more attention to this field from the global research community. [ABSTRACT FROM AUTHOR]

This paper presents an event-triggered model predictive control (MPC) strategy with learning terminal cost for robotic manipulators containing model uncertainty and input constraints. In the proposed MPC structure, an adaptive predictive model for the robotic system is established by radial basis function neural networks (RBFNNs) firstly. Then, a terminal cost adjusted by the global learning mechanism is constructed. Both global steady-state optimization and transient fast convergence are achieved by adding the learning terminal cost to the MPC scheme. After that, a triggering condition of the MPC solving is developed based on the predictive model’s weights and the predictive tracking error. Besides, the condition to avoid Zeno behavior is obtained. The recursive feasibility of the proposed MPC strategy is verified, and the ultimately uniformly boundedness (UUB) of all variables is proved according to the Lyapunov theorem. Finally, experiments based on an xMate7 Pro robot are conducted to demonstrate the effectiveness of the presented method. Note to Practitioners—The tracking control of robotic manipulators is a common and important problem in industrial applications, such as grasping, loading, unloading, et al. There exist some limitations in existing control methods. For example, general control strategies such as adaptive control, sliding mode control neglect the balance between control costs and expected performance; existing optimal control approaches rarely consider global steady-state optimization and transient fast convergence under the influence of model uncertainty. This paper is motivated by these limitations of robotic control design and inspired by model predictive control and optimal control theory. It develops a novel strategy for tracking control of robotic manipulators, which includes three following items: (1) adopt an approximation model constructed by neural networks as the predictive model for estimating the robotic system; (2) introduce a critic network into the terminal cost of the objective function of MPC for learning global optimal solution; (3) establish an event-triggered mechanism for solving of the optimization problem. The proposed method is verified by experimental results. In our future work, the proposed control strategy will be applied to more industrial processes. [ABSTRACT FROM AUTHOR]

Adaptation to the operator's intents and the external physical uncertainties is critical to the performances of the human–robot collaboration. In this study, we proposed a control interface that combined the electrical impedance tomography (EIT) based sensing approach with the robotic controllers to produce proper assistance with external uncertainties in the collaborative task. The interface first estimates the continuous forearm muscle contractions (represented by the grasp forces) by the optimized EIT features with an easily worn fabric band. The recognition decisions then serve as the control inputs to adjust the state transitions and the desired interaction forces in real time. We evaluated the interface in the tasks of grasp force estimation and human–robot sawing by recruiting more than 20 subjects in total. For grasp force estimation, the interface produced an average of $R^{2}\geq 0.9$ in both offline and online validations with the feature optimization procedure and a sigmoid regression function. The interface was robust to external disturbances without retraining or manual calibrations. The average $R^{2}=0.86$ with the untrained dynamic postures, and the average $R^{2}$ values ranged from 0.85 to 0.88 in the tests with redonning the front-end in interday and intraday uses. For human–robot sawing, the interface accomplished the tasks with a high success rate in controlling the states ($>$ 96%) and intuitive adjusting of the sawing forces, being combined with the designed hybrid admittance/position controller. It was also adaptive to the sawing frequency changes and the sawing directions according to the operator's intents. The interface's performances are comparable, if not better, to the state of the art on both biological signal based grasp force estimation and human–robot sawing. Future efforts are worth being paid in this new direction to get more promising outcomes. [ABSTRACT FROM AUTHOR]

This article proposes a novel decision-making framework that bridges a gap between model-based (MB) and model-free (MF) control processes through only adjusting the planning horizon. Specifically, the output policy is obtained by solving a model predictive control problem with a locally optimal state value as terminal constraints. When the planning horizon decreases to zero, the MB control will transform into the MF control smoothly. Meanwhile, inspired by the neural mechanism of emotion modulation on decision-making, we build a biologically plausible computational model of emotion processing. This model can generate an uncertainty-related emotional response on the basis of the state prediction error and reward prediction error, and then dynamically modulates the planning horizon in the tasks. The simulation results demonstrate that the proposed decision-making framework can produce better policies than traditional methods. Emotion modulation can shift the MB and MF control well to improve the learning efficiency and the speed of decision-making. [ABSTRACT FROM AUTHOR]

Owing to its potential superiorities in terms of flexibility, compliance, and robustness, the musculoskeletal robotic system has become a promising direction for next-generation robots. However, motion learning and generalization of musculoskeletal systems are still challenging problems. In this article, a muscle-synergies-based neuromuscular control is proposed. First, a new computational model of time-varying muscle synergies is constructed, which utilizes both phasic and tonic muscle synergies to characterize the basic features of muscle excitations more sufficiently. Second, a novel neuromuscular control method is proposed for realizing the motion learning and generalization of musculoskeletal systems. Therein, a radial basis function (RBF) neural network is designed to modulate muscle synergies according to different movement targets. Muscle excitations are computed with the combination of modulated muscle synergies. Covariance matrix adaptation evolutionary strategy is applied to realize the synchronous optimization of muscle synergies and the RBF neural network. In the experiment, a sophisticated musculoskeletal system learns to perform center-out reaching tasks through trial-and-error learning on a few targets. With the muscle synergies and neural modulation learned from a few targets, the musculoskeletal system can also reach many unexperienced targets. The proposed method not only improves the speed and accuracy of motion learning but also enhances motion generalization. This article also promotes the development of the musculoskeletal robotic system and the fusion of neuroscience and robotics. [ABSTRACT FROM AUTHOR]

Policy search in reinforcement learning (RL) is a practical approach to interact directly with environments in parameter spaces, that often deal with dilemmas of local optima and real-time sample collection. A promising algorithm, known as guided policy search (GPS), is capable of handling the challenge of training samples using trajectory-centric methods. It can also provide asymptotic local convergence guarantees. However, in its current form, the GPS algorithm cannot operate in sequential multitask learning scenarios. This is due to its batch-style training requirement, where all training samples are collectively provided at the start of the learning process. The algorithm’s adaptation is thus hindered for real-time applications, where training samples or tasks can arrive randomly. In this paper, the GPS approach is reformulated, by adapting a recently proposed, lifelong-learning method, and elastic weight consolidation. Specifically, Fisher information is incorporated to impart knowledge from previously learned tasks. The proposed algorithm, termed sequential multitask learning-GPS, is able to operate in sequential multitask learning settings and ensuring continuous policy learning, without catastrophic forgetting. Pendulum and robotic manipulation experiments demonstrate the new algorithms efficacy to learn control policies for handling sequentially arriving training samples, delivering comparable performance to the traditional, and batch-based GPS algorithm. In conclusion, the proposed algorithm is posited as a new benchmark for the real-time RL and robotics research community. [ABSTRACT FROM AUTHOR]

In various industries, there is a need for low-cost, flexible robotic grasping systems that handle and classify different items, which usually involve low-cost grippers and reliable manipulations. One-degree-offreedom (1-DoF) two-pin or three-pin grippers are widely used in the automation of manufacturing processes because of their low cost and high reliability. However, some uncertainties introduced by the object and the gripper may affect the robustness of the grasp. The principle of caging enables us to deal with these uncertainties, and it is therefore important to study caging as a practical solution to improve the grasps of 1-DoF industrial grippers. [ABSTRACT FROM AUTHOR]

A precise robotic assembly for large-scale objects is an important application in manufacture. In this paper, we design a new assembly system for large-scale objects based on automatic guidance and alignment with two cameras and three laser distance sensors. The corresponding strategy consists of three stages. First, the object is guided from a remote position to a close one based on our proposed link-line model-based polygon detection and Computer Aided Design (CAD) - View method. Second, the object is aligned to the installation framework based on the proposed visual and laser alignment method. Finally, the object is automatically inserted into the installation framework. An accurate easily applying and close-loop hand-eye calibration method is also proposed. The proposed assembly system has high assembly precision and efficiency. In our test for a 710 \text mm \times 625 mm optical module, the maximum 6-DOF localization error of guidance and alignment is 0.53 mm in translation and 0.10° in orientation. The mechanical constraint-based controlling accuracy, i.e., the final assembly accuracy, is up to 0.2 mm in translation. [ABSTRACT FROM PUBLISHER]

Development of a flexible low-cost robotic system to grasp various 3D workpieces is of practical important. Traditional approaches on 3D grasping usually need to compute the effective contact positions based on sufficient conditions, such as “force-closure” or “form-closure,” to prevent all the motions of the grasped objects. Compared with the previous work motivated by high-precision applications, caging provides a way to manipulate an object without needing to immobilize it. However, most caging conditions consider only 2D motions of the object in grasping. In some cases, other motions in 3D space, e.g., the pitch and roll rotations of the objects, would also possibly change a caging configuration to an uncaging configuration. This paper aims to discuss caging with frictionless contact, by taking into consideration of all the motions of the object. We first discuss the relationship between the gripper configuration and the state of the grasped object in grasping, where all the motions of the object are taken into account. We then establish the sufficient conditions to find a set of 3D caging configurations in terms of the width of the object's projection and the gap of the gripper, such that we can utilize the projection to find the feasible placements of the pins to determine the caging configuration. Furthermore, we discuss how to find the caging configuration that is capable of leading to a form-closure based on attractive region formed in the configuration space. [ABSTRACT FROM PUBLISHER]

In general, localization is a very important step in the manufacturing process, which can be considered as a prior process of assembly, machining, transportation, etc. Localization can be achieved with or without sensors. Compared with localization with sensors, localization without sensors can be more reliable, cheaper, and can have lower requirements on the environment. For example, localization without sensors can be achieved in a dark environment. However, it is more restrictive to the condition of the system. Localization of 2-D objects without sensors has been deeply investigated. Some theoretical results on 3-D-object localization without sensors have been given. On the other hand, some work has been carried out that tries to find methods to reduce uncertainties in the 3-D orientation of a polyhedron (i.e., the orientation of the polyhedron in a 3-D space). However, to the best of our knowledge, no practical and effective methods have been proposed, so far, to localize a polyhedron from any initial 3-D orientation to a unique 3-D orientation without sensors. This paper aims to find conditions and strategies for 3-D objects to be rotated from an unknown initial stable state to a unique stable state without sensors. The main contributions of this paper are given as follows: 1) It is discovered and proved that there are two classes of 3-D objects that can be rotated into a unique state from an arbitrary initial state without sensory feedback. 2) For these two classes of objects, the practical strategies are presented, and one example for each class is given to show the validity of the strategy. 3) Based on the above results, the robotic system and localization operations are illustrated, and some experimental results are given. [ABSTRACT FROM PUBLISHER]