Abstract-Robotic failure is all too common in unstructured robot tasks. Despite well-designed controllers, robots often fail due to unexpected events. Robots under a sense-planact paradigm do not have an additional loop to check their actions. In this work, we present a principled methodology to bootstrap online robot introspection for contact tasks. In effect, we seek to enable the robot to recognize and expect its behavior, else detect anomalies. We postulated that noisy wrench data inherently contains patterns that can be effectively represented by a vocabulary. The vocabulary is obtained by segmenting and encoding data. And when wrench information represents a sequence of sub-tasks, the vocabulary represents a set of words or sentence and provides a unique identifier. The grammar, which can also include unexpected events, was classified both offline and online for simulated and real robot experiments. Multi-class Support Vector Machines (SVMs) were used offline, while online probabilistic SVMs were used to give temporal confidence to the introspection result. Our work's contribution is the presentation of a generalizable online semantic scheme that enables a robot to understand its high-level state whether nominal or anomalous. It is shown to work in offline and online scenarios for a particularly challenging contact task: snap assemblies. We perform the snap assembly in one-arm simulated and real one-arm experiments and a simulated twoarm experiment. The data set itself is also fully available online and provides a valuable resource by itself for this type of contact task. Our verification mechanism can be used by highlevel planners or reasoning systems to enable intelligent failure recovery or determine the next most optimal manipulation skill to be used. Supplemental information, code, data, and other supporting documentation can be found at [1].

Robots are increasingly entering uncertain and unstructured environments. Within these, robots are bound to face unexpected external disturbances like accidental human or tool collisions. Robots must develop the capacity to respond to unexpected events. That is not only identifying the sudden anomaly, but also deciding how to handle it. In this work, we contribute a recovery policy that allows a robot to recovery from various anomalous scenarios across different tasks and conditions in a consistent and robust fashion. The system organizes tasks as a sequence of nodes composed of internal modules such as motion generation and introspection. When an introspection module flags an anomaly, the recovery strategy is triggered and reverts the task execution by selecting a target node as a function of a state dependency chart. The new skill allows the robot to overcome the effects of the external disturbance and conclude the task. Our system recovers from accidental human and tool collisions in a number of tasks. Of particular importance is the fact that we test the robustness of the recovery system by triggering anomalies at each node in the task graph showing robust recovery everywhere in the task. We also trigger multiple and repeated anomalies at each of the nodes of the task showing that the recovery system can consistently recover anywhere in the presence of strong and pervasive anomalous conditions. Robust recovery systems will be key enablers for long-term autonomy in robot systems. Supplemental information including videos, code, and result analysis can be found at [1].

Event detection is a critical feature in data-driven systems as it assists with the identification of nominal and anomalous behavior. Event detection is increasingly relevant in robotics as robots operate with greater autonomy in increasingly unstructured environments. In this work, we present an accurate, robust, fast, and versatile measure for skill and anomaly identification. A theoretical proof establishes the link between the derivative of the log-likelihood of the HMM filtered belief state and the latest emission probabilities. The key insight is the inverse relationship in which gradient analysis is used for skill and anomaly identification. Our measure showed better performance across all metrics than all but one related stateof-the-art works. The result is broadly applicable to domains that use HMMs for event detection. Supplemental information, code, data, and videos can be found at [1].

Introduction Tigecycline is a potential alternative to trimethoprim–sulfamethoxazole in treating Stenotrophomonas maltophilia infections due to its potent in vitro antimicrobial activity. Clinical evidence regarding the use of tigecycline in the treatment of S. maltophilia infections is scarce. In this study, we assessed the efficacy of tigecycline treating ventilator-associated pneumonia (VAP) due to S. maltophilia in comparison with fluoroquinolones. Methods This is a multicenter retrospective cohort study of patients admitted between January 2017 and December 2020 with the diagnosis of VAP caused by S. maltophilia receiving either tigecycline or fluoroquinolones as the definitive therapy ≥ 48 h. Clinical outcomes including 28-day mortality, clinical cure and microbiological cure were analyzed. Results Of 82 patients with S. maltophilia VAP included, 46 received tigecycline, and 36 received fluoroquinolones; 70.7% of patients had polymicrobial pneumonia, and the appropriate empiric therapy was applied to only 14.6% of patients. The overall 28-day mortality was 39%. Compared with patients receiving fluoroquinolones, tigecycline therapy resulted in worse clinical cure (32.6% vs. 63.9%, p = 0.009) and microbiological cure (28.6% vs. 59.1%, p = 0.045), while there was no statistical difference between 28-day mortality (47.8% vs. 27.8%, p = 0.105) in the two groups. Similar results were also shown in the inverse probability of treatment weighted univariable regression model and multivariable regression model. Conclusions The standard dose of tigecycline therapy was associated with a lower clinical and microbiological cure rate but not associated with an increased 28-day mortality in patients with S. maltophilia VAP compared with fluoroquinolones. Considering the unfavorable clinical outcomes, we therefore recommend against using the standard dose of tigecycline in treating S. maltophilia VAP unless new clinical evidence emerges. Supplementary Information The online version contains supplementary material available at 10.1007/s40121-021-00516-5.

Robot manipulation is increasingly poised to interact with humans in co-shared workspaces. Despite increasingly robust manipulation and control algorithms, failure modes continue to exist whenever models do not capture the dynamics of the unstructured environment. To obtain longer-term horizons in robot automation, robots must develop introspection and recovery abilities. We contribute a set of recovery policies to deal with anomalies produced by external disturbances as well as anomaly classification through the use of non-parametric statistics with memoized variational inference with scalable adaptation. A recovery critic stands atop of a tightly-integrated, graph-based online motion-generation and introspection system that resolves a wide range of anomalous situations. Policies, skills, and introspection models are learned incrementally and contextually in a task. Two task-level recovery policies: re-enactment and adaptation resolve accidental and persistent anomalies respectively. Re-enactment policies model human decision making to reenact the best skill in the task-graph. Adaptive recoveries leverage human intuition about the task-state to overcome persistent errors. The system is capable of fast and robust anomaly identification and classification during all phases of a task including during the execution of newly learned recovery skills. The introspection system uses non-parametric priors along with Markov jump linear systems and memoized variational inference with scalable adaptation to learn a model from the data in an incremental way and yield compact interpretable models that enhance classification and identification accuracy. Extensive real-robot experimentation with various strenuous anomalous conditions in a co-bot scenario is induced and resolved at different phases of a task and in different combinations. The system executes around-the-clock introspection and recovery and even elicited self-recovery when misclassifications occurred.