A capsule that could replace insulin shots. In Giovanni Traverso's lab, the focus is always on making life better for patients. Every Monday, more than a hundred members of Giovanni Traverso's Laboratory for Translational Engineering (L4TE) fill a large classroom at Brigham and Women's Hospital for their weekly lab meeting. With a social hour, food for everyone, and updates across disciplines from mechanical engineering to veterinary science, it's a place where a stem cell biologist might weigh in on a mechanical design, or an electrical engineer might spot a flaw in a drug delivery mechanism. And it's a place where everyone is united by the same goal: engineering new ways to deliver medicines and monitor the body to improve patient care. Traverso's weekly meetings bring together a mix of expertise that lab members say is unusual even in the most collaborative research spaces. But his lab--which includes its own veterinarian and a dedicated in vivo team--isn't built like most.

Since 1999, the opioid epidemic has killed around 645,000 people in America--a number that would no doubt be even higher were it not for naloxone, an opioid antagonist that can effectively reverse the effects of an overdose. However, time is critical: if naloxone is not administered promptly, the victim's chances of survival diminish rapidly. In a paper published August 14 in Device, a team of researchers describe a device designed to detect the signs of an overdose and automatically deliver a dose of naloxone in as little as 10 seconds. The device–which researchers describe as a "robotic first responder"–is named the "implantable system for opioid safety" (iSOS). It's implanted under the user's skin, in the same way as a heart loop recorder.

With the Covid-19 pandemic hitting hard and social distancing becoming a vital norm, this opens the door for using more robots to provide health care services to reduce in-person contact between the health care workers and the patients. Giovanni Traverso, an MIT assistant professor of mechanical engineering, a gastroenterologist at Brigham and Women's Hospital, and also the senior author of the study said, that they were actively working on robots that can help provide health care services to maximize the safety, of both the patients and the health care workforce. Traverso and his colleagues after the Covid-19 began last year, worked towards reducing interaction between the patients and the health care workers. In this process, they collaborated with Boston Dynamics in creating mobile robots that can interact with patients who waited in the emergency department. But the question here is, how patients are going to respond to the robots?

In the era of social distancing, using robots for some health care interactions is a promising way to reduce in-person contact between health care workers and sick patients. However, a key question that needs to be answered is how patients will react to a robot entering the exam room. Researchers from MIT and Brigham and Women's Hospital recently set out to answer that question. In a study performed in the emergency department at Brigham and Women's, the team found that a large majority of patients reported that interacting with a health care provider via a video screen mounted on a robot was similar to an in-person interaction with a health care worker. "We're actively working on robots that can help provide care to maximize the safety of both the patient and the health care workforce. The results of this study give us some confidence that people are ready and willing to engage with us on those fronts," says Giovanni Traverso, an MIT assistant professor of mechanical engineering, a gastroenterologist at Brigham and Women's Hospital, and the senior author of the study.

Researchers from MIT and Brigham and Women's Hospital recently set out to answer that question. In a study performed in the emergency department at Brigham and Women's, the team found that a large majority of patients reported that interacting with a health care provider via a video screen mounted on a robot was similar to an in-person interaction with a health care worker. "We're actively working on robots that can help provide care to maximize the safety of both the patient and the health care workforce. The results of this study give us some confidence that people are ready and willing to engage with us on those fronts," says Giovanni Traverso, an MIT assistant professor of mechanical engineering, a gastroenterologist at Brigham and Women's Hospital, and the senior author of the study. In a larger online survey conducted nationwide, the researchers also found that a majority of respondents were open to having robots not only assist with patient triage but also perform minor procedures such as taking a nose swab.

We contribute to recent efforts in relating two approaches to automatic synthesis, namely, automated planning and discrete reactive synthesis. First, we develop a declarative characterization of the standard "fairness" assumption on environments in non-deterministic planning, and show that strong-cyclic plans are correct solution concepts for fair environments. This complements, and arguably completes, the existing foundational work on non-deterministic planning, which focuses on characterizing (and computing) plans enjoying special "structural" properties, namely loopy but closed policy structures. Second, we provide an encoding suitable for reactive synthesis that avoids the naive exponential state space blowup. To do so, special care has to be taken to specify the fairness assumption on the environment in a succinct manner.

"The problem is not that nobody has Kinect, but it's that nobody is talking about it anymore." Traverso has a unique perspective on the Kinect marketplace because he's one of the last video-game developers to build an experience specifically for Microsoft's motion-sensing peripheral. Not that the Kinect is officially dead. However, Kinect is clearly not a priority for Microsoft. In 2016, the Xbox One's Kinect 2 received just two games from third-party studios, Fru and Just Dance 2017.

Whereas previous work on non-deterministic planning has focused on characterizing (and computing) "loopy" but "closed" plans, we look here at the kind of environments that these plans are to be executed in. In particular, we provide a logical characterization of the standard "fairness'' assumption used, and show that strong cyclic plans are correct solution concepts for fair environments.  We argue then that such logical characterization allows us to recast non-deterministic planning as a reactive synthesis task, and show that for a special case, recent efficient synthesis techniques can be applied.

Planning is concerned with the automated solution of action sequencing problems described in declarative languages giving the action preconditions and effects. One important application area for such technology is the creation of new processes in Business Process Management (BPM), which is essential in an ever more dynamic business environment. A major obstacle for the application of Planning in this area lies in the modeling. Obtaining a suitable model to plan with -- ideally a description in PDDL, the most commonly used planning language -- is often prohibitively complicated and/or costly. Our core observation in this work is that this problem can be ameliorated by leveraging synergies with model-based software development. Our application at SAP, one of the leading vendors of enterprise software, demonstrates that even one-to-one model re-use is possible. The model in question is called Status and Action Management (SAM). It describes the behavior of Business Objects (BO), i.e., large-scale data structures, at a level of abstraction corresponding to the language of business experts. SAM covers more than 400 kinds of BOs, each of which is described in terms of a set of status variables and how their values are required for, and affected by, processing steps (actions) that are atomic from a business perspective. SAM was developed by SAP as part of a major model-based software engineering effort. We show herein that one can use this same model for planning, thus obtaining a BPM planning application that incurs no modeling overhead at all. We compile SAM into a variant of PDDL, and adapt an off-the-shelf planner to solve this kind of problem. Thanks to the resulting technology, business experts may create new processes simply by specifying the desired behavior in terms of status variable value changes: effectively, by describing the process in their own language.

Model update is a formal approach to correct a system model M w.r.t some property not satisfied by M. In this work, we show how this formal approach can be used for plan and planning domain verification and update. While a model checking method can directly be used to perform plan verification, model update techniques can be used to either update an incorrect plan and\or update a planning domain specification. Well known model update approaches are based on CTL — a logic which does not take into account the actions. In previous work, we have proposed the alpha-CTL logic, a logic whose semantics is based on actions. Here, we are proposing a model update system based on alpha-CTL which is able to automatically modify a plan M, generating a new plan M' that satisfies phi or, if there is not such a plan, to automatically update the corresponding planning domain.