Inside a Schnucks grocery store in St. Louis, Missouri, the toilet paper and baking ingredients are mostly cleared out. A rolling robot turns a corner and heads down an aisle stocked with salsa and taco shells. It comes up against a masked customer wearing shorts and sneakers; he's pushing a shopping cart carrying bread. The robot looks something like a tower speaker on top of an autonomous home vacuum cleaner--tall and thin, with orb-like screen eyes halfway up that shift left and right. A red sign on its long head makes the introductions. Tally freezes, sensing the human, and the customer pauses, seeming unsure of what to do next. Should he maneuver around the robot? Or wait for it to move along on its own?

Diligent Robotics today announced the close of a $10 million round to expand its fleet of nurse assistant robots for hospitals. The round was led by DNX Ventures, with participation from True Ventures, Ubiquity Ventures, Next Coast Ventures, Grit Ventures, E14 Fund, and Promus Ventures. Moxi is designed to reduce nurse workloads by handling tasks like collecting supplies, gathering soiled linens, and delivering fresh ones, and it's coming to market during the COVID-19 crisis, when nurses are in short supply. In addition to tackling mundane aspects of the job, the robot can also help reduce health care professionals' exposure to disease. Moxi was created by Diligent Robotics at University of Texas, Austin by CEO Dr. Andrea Thomaz, a roboticist and professor who previously ran the Georgia Tech Socially Intelligent Machines Lab. "It's a really good time to be working on this problem," Thomaz told VentureBeat in a phone interview.

Diligent Robotics's Moxi is a robot created by Andrea Thomaz (a former robotics professor at UT Austin and Georgia Tech's Socially Intelligent Machines Lab) and Vivian Chu (one of Thomaz's former grad students); they funded by a National Science Foundation grant to create a robotic nursing aide that is designed to do routine, non-human-interaction chores for nurses with a minimum of effort from nurses. For example, when a patient is discharged, a Moxi can fetch and deliver an "admission bucket" (a standard package of supplies for a new patient) automatically; some nurses in a limited trial say they never saw Moxi undertake this chore, but rather simply found that every recently vacated room had an admission bucket waiting at the appropriate time. The design is meant to relieve nurses of mechanical, robotic tasks (errands) and free them up to concentrate on care and humans. After four one-month beta trials, the company says Moxi robots do that very well, but they were surprised by the affection that both nurses and patients expressed for the robots, which was so intense that the technicians began to schedule an hour-long "social lap," in which the robot wanders around and makes heart-emoji eyes at patients. The physical design of Moxi reflects Thomaz's "Socially Intelligent Machines" research; they are designed to be cute and nonthreatening, and to give social cues that humans intuitively grasp, like looking in the direction they're moving.

But Moxi, which was designed and built by the Austin-based company Diligent Robotics, isn't trying to act like a nurse. Instead, Diligent Robotics founders Andrea Thomaz and Vivian Chu have designed their robot to run the approximately 30% of tasks nurses do that don't involve interacting with patients, like running errands around the floor or dropping off specimens for analysis at a lab. "We're helping them augment their staff," says Thomaz, who formerly was a robotics professor at UT Austin and Georgia Tech, where she ran the Socially Intelligent Machines Lab. "It's hard to argue that we're taking anyone's job. Everyone is trying to make the nurses they have go further." Moxi is equipped with a robotic arm and a set of wheels on its base, and can be preprogrammed to run errands around the hospital.

Researchers in Texas are developing robots that have minds of their own. The scientists are creating systems that can learn for themselves and be able to operate in the home, the workplace and even on the sports field. The University of Texas, Austin team is incorporating artificial intelligence into its machines so that they can deal with real-world situations. Science fiction films predicted that in the future we would have intelligent robots. In the Day the Earth Stood Still, we had the sinister Gort; in Forbidden Planet there was Robby; and in the TV series Lost In Space it was Zachary Smith's nemesis, the Robot.

She was smart, feisty, and sometimes pensive. Sam was easy to talk to and brimming with personality. The AI from Spike Jonze's 2013 movie caught our attention not just because it had the knowledge base of a thousand IBM Watsons, but also because conversations with Samantha were like chats with a close friend. Over the last few years, robot researchers Dr. Crystal Chao and Professor Andrea Thomaz at Georgia Tech have been devising a new way to build humanity and personality into human-robot dialogues. It starts with rethinking the way we talk to machines.

Semester exams are looming, with an extended holiday break on their heels. But before Dr. Andrea Thomaz closes the Socially Intelligent Machines Lab at the Georgia Institute of Technology for the season, the lab hosted a few more visitors last week for the final experiment of the semester. The lab welcomes guests to interact with Simon, a humanoid robot developed with seed funds from the Office of Naval Research (ONR). These interactions allow student-researchers to adjust software models for Simon's learning and behavior generation. And it all starts once Thomaz and her team wake the resting robot.

When Seton Medical Center Austin earlier this week unveiled Poli, its newest nurse's aide-in-training, the robot looked as if it had been lifted from a Hollywood backlot, but with one notable difference: It spoke with a child's voice. Functionally, Poli is still a child. She still relies on lessons imparted by her creators as she navigates the world and learns from her experiences. Somewhere along the way, Poli picked up a hint of teenage martyrdom. "I'm sleepy," she said at one point.

Seton Medical Center Austin is taking a first, cautious step into the world of robots programmed to function like people. This week, the hospital system introduced Poli, a University of Texas-designed robot that -- once the bugs are worked out -- will assist nurses as they go about the business of taking care of people. Poli is vaguely anthropomorphic, with a head and a body but a limited ability to speak. It moves about on wheels and grabs things with a grasping arm. The robot won't be working directly with patients, Seton officials say.

The goal of this research is to enable robots to learn new things from everyday people. For years, the AI and Robotics community has sought to enable robots to efficiently learn new skills from a knowledgeable human trainer, and prior work has focused on several important technical problems. This vast amount of research in the field of robot Learning by Demonstration has by and large only been evaluated with expert humans, typically the system's designer. Thus, neglecting a key point that this interaction takes place within a social structure that can guide and constrain the learning problem. %Moreover, we We believe that addressing this point will be essential for developing systems that can learn from everyday people that are not experts in Machine Learning or Robotics. Our work focuses on new research questions involved in letting robots learn from everyday human partners (e.g., What kind of input do people want to provide a machine learner? How does their mental model of the learning process affect this input? What interfaces and interaction mechanisms can help people provide better input from a machine learning perspective?) Often our research begins with an investigation into the feasibility of a particular machine learning interaction, which leads to a series of research questions around re-designing both the interaction and the algorithm to better suit learning with end-users. We believe this equal focus on both the Machine Learning and the HRI contributions are key to making progress toward the goal of machines learning from humans. In this abstract we briefly overview four different projects that highlight our HRI approach to the problem of Learning from Demonstration.