Preference-based learning aims to align robot task objectives with human values. One of the most common methods to infer human preferences is by pairwise comparisons of robot task trajectories. Traditional comparison-based preference labeling systems seldom support labelers to digest and identify critical differences between complex trajectories recorded in videos. Our formative study (N = 12) suggests that individuals may overlook non-salient task features and establish biased preference criteria during their preference elicitation process because of partial observations. In addition, they may experience mental fatigue when given many pairs to compare, causing their label quality to deteriorate. To mitigate these issues, we propose FARPLS, a Feature-Augmented Robot trajectory Preference Labeling System. FARPLS highlights potential outliers in a wide variety of task features that matter to humans and extracts the corresponding video keyframes for easy review and comparison. It also dynamically adjusts the labeling order according to users’ familiarities, difficulties of the trajectory pair, and level of disagreements. At the same time, the system monitors labelers’ consistency and provides feedback on labeling progress to keep labelers engaged. A between-subjects study (N = 42, 105 pairs of robot pick-and-place trajectories per person) shows that FARPLS can help users establish preference criteria more easily and notice more relevant details in the presented trajectories than the conventional interface. FARPLS also improves labeling consistency and engagement, mitigating challenges in preference elicitation without raising cognitive loads significantly.

Deadlock is always a challenging problem for multi-agent pathfinding, especially when the system is in high scales in terms of number of agents and map size. Some recent studies showed that the agents can learn to resolve the deadlock problem through reinforcement learning. However, most of them are not designed for non-holonomic robots, which are commonly applied in warehouses. In particular, the rotation movement may cause the agents staying at the same locations for a long time, and the deadlock happens more frequently especially in dense environment. In this paper, an algorithm called MAPF-rot with a deadlock breaking scheme is proposed to tackle the deadlock problem arising from the rotation movement in the multi-agent pathfinding problem. Experiments are performed to demonstrate the efficiency of the proposed algorithm.

Condition monitoring and fault diagnosis in modern manufacturing automation is of great practical significance. It improves quality and productivity, and prevents damage to machinery. In general, this practice consists of two parts: 1)extracting appropriate features from sensor signals and 2)recognizing possible faulty patterns from the features. Through introducing the concept of marginal energy in signal processing, a new feature representation is developed in this paper. In order to cope with the complex manufacturing operations, three approaches are proposed to develop a feasible system for online applications. This paper develops intelligent learning algorithms using hidden Markov models and the newly developed support vector techniques to model manufacturing operations. The algorithms have been coded in modular architecture and hierarchical architecture for the recognition of multiple faulty conditions. We define a novel similarity measure criterion for the comparison of signal patterns which will be incorporated into a novel condition monitoring system. The sensor-based intelligent system has been implemented in stamping operations as an example. We demonstrate that the proposed method is substantially more effective than the previous approaches. Its unique features benefit various real-world manufacturing automation engineering, and it has great potential for shop floor applications.

Condition monitoring and fault diagnosis in modern engineering practices is of great practical significance for improving the quality and productivity, preventing the machinery from damages. In general, this practice consists of two parts: extracting appropriate features from sensor signals and recognizing possible faulty patterns from the features. In order to cope with the complex manufacturing operations and develop a feasible system for real-time application, we proposed three approaches. By defining the marginal energy, a new feature representation emerged, while by real-time learning algorithms with support vector techniques and hidden Markov model representations, a modular software architecture and a new similarity measure were developed for comparison, monitoring, and diagnosis. A novel intelligent computer-based system has been developed and evaluated in over 30 factories and numerous metal stamping processes as an example of manufacturing operations. The real-time operation of this system demonstrated that the proposed system is able to detect abnormal conditions efficiently and effectively resulting in a low-cost, effective approach to real-time monitoring in manufacturing. The related technologies have been transferred to industry, presenting a tremendous impact in current automation practice in Asia and the world.

The real-time fault detection and diagnosis are critical for healthy operation of electromechanical systems, of which the complex characteristics affect the performance of current shop floor fault diagnosis methods. Aiming to overcome the drawbacks, this paper presents a new fault diagnosis method using a newly developed method, support vector machines (SVM). First, the basic theory of SVM is briefly introduced and new intelligent fault diagnosis system is presented. Next, three common SVM algorithms - v-SV, Lagrangian, and hyper-kernel - are employed for the proposed multiple faults diagnosis system. In comparison, the trade-offs among these three methods are discussed resulting in a general guideline of selecting appropriate learning algorithm for various applications. Then, the methods are applied for diagnosing vibration signals of a typical electromechanical system, elevator door. The real-time tests on 10 faulty conditions demonstrate that the proposed method is effective and efficient. In addition, the method requires only few training samples and permits fast calculation, giving it a big potential in real-world applications.