Robot-Assisted Pedestrian Regulation Based on Deep Reinforcement Learning

Human-in-the-Loop Optimization of Wearable Robotic Devices to Improve Human-Robot Interaction: A Systematic Review

This article presents a systematic review on wearable robotic devices that use human-in-the-loop optimization (HILO) strategies to improve human-robot interaction. A total of 46 HILO studies were identified and divided into upper and lower limb robotic devices. The main aspects from HILO were identified, reviewed, and classified in four areas: 1) human-machine systems; 2) optimization methods; 3) control strategies; and 4) experimental protocols. A variety of objective functions (physiological, biomechanical, and subjective), optimization strategies, and optimized control parameters configurations used in different control strategies are presented and analyzed. An overview of experimental protocols is provided, including metrics, tasks, and conditions tested. Moreover, the relevance given to training or adaptation periods was explored. We outline an HILO framework that includes current wearable robots, optimization strategies, objective functions, control strategies, and experimental protocols. We conclude by highlighting current research gaps and defining future directions to improve the development of advanced HILO strategies in upper and lower limb wearable robots.

Complex Robotic Manipulation via Graph-Based Hindsight Goal Generation

Reinforcement learning algorithms, such as hindsight experience replay (HER) and hindsight goal generation (HGG), have been able to solve challenging robotic manipulation tasks in multigoal settings with sparse rewards. HER achieves its training success through hindsight replays of past experience with heuristic goals but underperforms in challenging tasks in which goals are difficult to explore. HGG enhances HER by selecting intermediate goals that are easy to achieve in the short term and promising to lead to target goals in the long term. This guided exploration makes HGG applicable to tasks in which target goals are far away from the object's initial position. However, the vanilla HGG is not applicable to manipulation tasks with obstacles because the Euclidean metric used for HGG is not an accurate distance metric in such an environment. Although, with the guidance of a handcrafted distance grid, grid-based HGG can solve manipulation tasks with obstacles, a more feasible method that can solve such tasks automatically is still in demand. In this article, we propose graph-based hindsight goal generation (G-HGG), an extension of HGG selecting hindsight goals based on shortest distances in an obstacle-avoiding graph, which is a discrete representation of the environment. We evaluated G-HGG on four challenging manipulation tasks with obstacles, where significant enhancements in both sample efficiency and overall success rate are shown over HGG and HER. Videos can be viewed at https://videoviewsite.wixsite.com/ghgg.

Deep Temporal Model-Based Identity-Aware Hand Detection for Space Human-Robot Interaction

Hand detection is a crucial technology for space human-robot interaction (SHRI), and the awareness of hand identities is particularly critical. However, most advanced works have three limitations: 1) the low detection accuracy of small-size objects; 2) insufficient temporal feature modeling between frames in videos; and 3) the inability of real-time detection. In the article, a temporal detector (called TA-RSSD) is proposed based on the SSD and spatiotemporal long short-term memory (ST-LSTM) for real-time detection in SHRI applications. Next, based on the online tubelet analysis, a real-time identity-awareness module is designed for multiple hand object identification. Several notable properties are described as follows: 1) the hybrid structure of the Resnet-101 and the SSD improves the detection accuracy of small objects; 2) three-level feature pyramidal structure retains rich semantic information without losing detailed information; 3) a group of the redesigned temporal attentional LSTM (TA-LSTM) is utilized for three-level feature map modeling, which effectively achieves background suppression and scale suppression; 4) low-level attention maps are used to eliminate in-class similarity between hand objects, which improves the accuracy of identity awareness; and 5) a novel association training scheme enhances the temporal coherence between frames. The proposed model is evaluated on the SHRI-VID dataset (collected according to the task requirements), the AU-AIR dataset, and the ImageNet-VID benchmark. Extensive ablation studies and comparisons on detection and identity-awareness capacities show the superiority of the proposed model. Finally, a set of actual testing is conducted on a space robot, and the results show that the proposed model achieves a real-time speed and high accuracy.

A novel selected force controlling method for improving robotic grinding accuracy of complex curved blade

Nonlinear time-varying contact state is a crucial factor to prevent the traditional robotic belt grinding method from precision machining of blade. In this case, a novel selected force controlling method (SFC) with consideration of regional division (RD) based on machining allowance is proposed for improving robotic grinding accuracy of complex curved blade, on basis of the self-developed adaptive impedance controller. Ideal normal grinding force at each cutter-contact (CC) point is calculated by principal curvature radius and regional allowance of blade surface. Then, the CC points with similar ideal normal grinding force are divided into one region along grinding path based on the force threshold. Furthermore, an adaptive impedance controller with neural network online compensation algorithm (AICNN) is developed, and the verification test results of grinding four profile areas of intake side, exhaust side, convex and concave, indicate that the force control accuracy with AICNN has increased by 80.33%, 50.58%, 82.65% and 69.01% than that without the controller, respectively. Based on this, the grinding experiment of typical turbine blade is conducted with SFC, and the surface profile accuracy values at the four profile areas have evidently improved by 48.79%, 35.67%, 59.54%, and 66.90% than that with conventional grinding (CG), respectively.

Solver-Critic: A Reinforcement Learning Method for Discrete-Time-Constrained-Input Systems

In this article, a solver-critic (SC) architecture is developed for optimal control problems of discrete-time (DT)-constrained-input systems. The proposed design consists of three parts: 1) a critic network; 2) an action solver; and 3) a target network. The critic network first approximates the action-value function using the sum-of-squares (SOS) polynomial. Then, the action solver adopts the SOS programming to obtain control inputs within the constraint set. The target network introduces the soft update mechanism into policy evaluation to stabilize the learning process. By using the proposed architecture, the constrained-input control problem can be solved without adding the nonquadratic functionals into the reward function. In this article, the theoretical analysis of the convergence property is presented. Besides, the effects of both different initial Q -functions and different discount factors are investigated. It is proven that the learned policy converges to the optimal solution of the Hamilton-Jacobi-Bellman equation. Four numerical examples are provided to validate the theoretical analysis and also demonstrate the effectiveness of our approach.

Dual Alignment for Partial Domain Adaptation

Partial domain adaptation (PDA) aims to transfer knowledge from a label-rich source domain to a label-scarce target domain based on an assumption that the source label space subsumes the target label space. The major challenge is to promote positive transfer in the shared label space and circumvent negative transfer caused by the large mismatch across different label spaces. In this article, we propose a dual alignment approach for PDA (DAPDA), including three components: 1) a feature extractor extracts source and target features by the Siamese network; 2) a reweighting network produces "hard" labels, class-level weights for source features and "soft" labels, instance-level weights for target features; 3) a dual alignment network aligns intra domain and interdomain distributions. Specifically, the intra domain alignment aims to minimize the intraclass variances to enhance the intraclass compactness in both domains, and interdomain alignment attempts to reduce the discrepancies across domains by domain-wise and class-wise adaptations. The negative transfer can be alleviated by down-weighting source features with nonshared labels. The positive transfer can be enhanced by upweighting source features with shared labels. The adaptation can be achieved by minimizing the discrepancies based on class-weighted source data with hard labels and instance-weighed target data with soft labels. The effectiveness of our method has been demonstrated by outperforming state-of-the-art PDA methods on several benchmark datasets.

Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications

Reinforcement learning (RL) algorithms have been around for decades and employed to solve various sequential decision-making problems. These algorithms, however, have faced great challenges when dealing with high-dimensional environments. The recent development of deep learning has enabled RL methods to drive optimal policies for sophisticated and capable agents, which can perform efficiently in these challenging environments. This article addresses an important aspect of deep RL related to situations that require multiple agents to communicate and cooperate to solve complex tasks. A survey of different approaches to problems related to multiagent deep RL (MADRL) is presented, including nonstationarity, partial observability, continuous state and action spaces, multiagent training schemes, and multiagent transfer learning. The merits and demerits of the reviewed methods will be analyzed and discussed with their corresponding applications explored. It is envisaged that this review provides insights about various MADRL methods and can lead to the future development of more robust and highly useful multiagent learning methods for solving real-world problems.