Interview with Kate Candon: Leveraging express and implicit suggestions in human-robot interactions

On this interview collection, we’re assembly a few of the AAAI/SIGAI Doctoral Consortium members to seek out out extra about their analysis. Kate Candon is a PhD pupil at Yale College excited about understanding how we are able to create interactive brokers which can be extra successfully capable of assist folks. We spoke to Kate to seek out out extra about how she is leveraging express and implicit suggestions in human-robot interactions.

May you begin by giving us a fast introduction to the subject of your analysis?

I research human-robot interplay. Particularly I’m excited about how we are able to get robots to higher study from people in the best way that they naturally educate. Sometimes, a whole lot of work in robotic studying is with a human trainer who is just tasked with giving express suggestions to the robotic, however they’re not essentially engaged within the process. So, for instance, you might need a button for “good job” and “unhealthy job”. However we all know that people give a whole lot of different alerts, issues like facial expressions and reactions to what the robotic’s doing, possibly gestures like scratching their head. It might even be one thing like shifting an object to the aspect {that a} robotic palms them – that’s implicitly saying that that was the mistaken factor at hand them at the moment, as a result of they’re not utilizing it proper now. These implicit cues are trickier, they want interpretation. Nonetheless, they’re a option to get extra info with out including any burden to the human person. Previously, I’ve checked out these two streams (implicit and express suggestions) individually, however my present and future analysis is about combining them collectively. Proper now, we’ve got a framework, which we’re engaged on enhancing, the place we are able to mix the implicit and express suggestions.

By way of selecting up on the implicit suggestions, how are you doing that, what’s the mechanism? As a result of it sounds extremely troublesome.

It may be actually laborious to interpret implicit cues. Folks will reply otherwise, from individual to individual, tradition to tradition, and so forth. And so it’s laborious to know precisely which facial response means good versus which facial response means unhealthy.

So proper now, the primary model of our framework is simply utilizing human actions. Seeing what the human is doing within the process may give clues about what the robotic ought to do. They’ve completely different motion areas, however we are able to discover an abstraction in order that we are able to know that if a human does an motion, what the same actions can be that the robotic can do. That’s the implicit suggestions proper now. After which, this summer season, we wish to prolong that to utilizing visible cues and facial reactions and gestures.

So what sort of situations have you ever been form of testing it on?

For our present undertaking, we use a pizza making setup. Personally I actually like cooking for instance as a result of it’s a setting the place it’s simple to think about why these items would matter. I additionally like that cooking has this factor of recipes and there’s a method, however there’s additionally room for private preferences. For instance, anyone likes to place their cheese on high of the pizza, so it will get actually crispy, whereas different folks wish to put it underneath the meat and veggies, in order that possibly it’s extra melty as a substitute of crispy. And even, some folks clear up as they go versus others who wait till the tip to cope with all of the dishes. One other factor that I’m actually enthusiastic about is that cooking may be social. Proper now, we’re simply working in dyadic human-robot interactions the place it’s one particular person and one robotic, however one other extension that we wish to work on within the coming 12 months is extending this to group interactions. So if we’ve got a number of folks, possibly the robotic can study not solely from the particular person reacting to the robotic, but additionally study from an individual reacting to a different particular person and extrapolating what that may imply for them within the collaboration.

May you say a bit about how the work that you just did earlier in your PhD has led you thus far?

After I first began my PhD, I used to be actually excited about implicit suggestions. And I assumed that I wished to give attention to studying solely from implicit suggestions. Certainly one of my present lab mates was centered on the EMPATHIC framework, and was trying into studying from implicit human suggestions, and I actually preferred that work and thought it was the path that I wished to enter.

Nonetheless, that first summer season of my PhD it was throughout COVID and so we couldn’t actually have folks come into the lab to work together with robots. And so as a substitute I did a web-based research the place I had folks play a sport with a robotic. We recorded their face whereas they have been taking part in the sport, after which we tried to see if we might predict primarily based on simply facial reactions, gaze, and head orientation if we might predict what behaviors they most well-liked for the agent that they have been taking part in with within the sport. We truly discovered that we might decently effectively predict which of the behaviors they most well-liked.

The factor that was actually cool was we discovered how a lot context issues. And I believe that is one thing that’s actually essential for going from only a solely teacher-learner paradigm to a collaboration – context actually issues. What we discovered is that typically folks would have actually large reactions however it wasn’t essentially to what the agent was doing, it was to one thing that that they had executed within the sport. For instance, there’s this clip that I all the time use in talks about this. This particular person’s taking part in and she or he has this actually noticeably confused, upset look. And so at first you may suppose that’s adverse suggestions, regardless of the robotic did, the robotic shouldn’t have executed that. However in case you truly have a look at the context, we see that it was the primary time that she misplaced a life on this sport. For the sport we made a multiplayer model of Area Invaders, and she or he bought hit by one of many aliens and her spaceship disappeared. And so primarily based on the context, when a human seems at that, we truly say she was simply confused about what occurred to her. We wish to filter that out and never truly take into account that when reasoning concerning the human’s conduct. I believe that was actually thrilling. After that, we realized that utilizing implicit suggestions solely was simply so laborious. That’s why I’ve taken this pivot, and now I’m extra excited about combining the implicit and express suggestions collectively.

You talked about the express factor can be extra binary, like good suggestions, unhealthy suggestions. Would the person-in-the-loop press a button or would the suggestions be given via speech?

Proper now we simply have a button for good job, unhealthy job. In an HRI paper we checked out express suggestions solely. We had the identical house invaders sport, however we had folks come into the lab and we had somewhat Nao robotic, somewhat humanoid robotic, sitting on the desk subsequent to them taking part in the sport. We made it in order that the particular person might give optimistic or adverse suggestions throughout the sport to the robotic in order that it might hopefully study higher serving to conduct within the collaboration. However we discovered that individuals wouldn’t truly give that a lot suggestions as a result of they have been centered on simply making an attempt to play the sport.

And so on this work we checked out whether or not there are other ways we are able to remind the particular person to offer suggestions. You don’t wish to be doing it on a regular basis as a result of it’ll annoy the particular person and possibly make them worse on the sport in case you’re distracting them. And likewise you don’t essentially all the time need suggestions, you simply need it at helpful factors. The 2 situations we checked out have been: 1) ought to the robotic remind somebody to offer suggestions earlier than or after they struggle a brand new conduct? 2) ought to they use an “I” versus “we” framing? For instance, “keep in mind to offer suggestions so I generally is a higher teammate” versus “keep in mind to offer suggestions so we generally is a higher group”, issues like that. And we discovered that the “we” framing didn’t truly make folks give extra suggestions, however it made them really feel higher concerning the suggestions they gave. They felt prefer it was extra useful, form of a camaraderie constructing. And that was solely express suggestions, however we wish to see now if we mix that with a response from somebody, possibly that time can be an excellent time to ask for that express suggestions.

You’ve already touched on this however might you inform us concerning the future steps you will have deliberate for the undertaking?

The massive factor motivating a whole lot of my work is that I wish to make it simpler for robots to adapt to people with these subjective preferences. I believe by way of goal issues, like with the ability to choose one thing up and transfer it from right here to right here, we’ll get to a degree the place robots are fairly good. However it’s these subjective preferences which can be thrilling. For instance, I like to prepare dinner, and so I need the robotic to not do an excessive amount of, simply to possibly do my dishes while I’m cooking. However somebody who hates to prepare dinner may need the robotic to do all the cooking. These are issues that, even you probably have the proper robotic, it could’t essentially know these issues. And so it has to have the ability to adapt. And a whole lot of the present choice studying work is so knowledge hungry that it’s important to work together with it tons and tons of occasions for it to have the ability to study. And I simply don’t suppose that that’s lifelike for folks to really have a robotic within the residence. If after three days you’re nonetheless telling it “no, if you assist me clear up the lounge, the blankets go on the sofa not the chair” or one thing, you’re going to cease utilizing the robotic. I’m hoping that this mixture of express and implicit suggestions will assist it’s extra naturalistic. You don’t must essentially know precisely the fitting option to give express suggestions to get the robotic to do what you need it to do. Hopefully via all of those completely different alerts, the robotic will be capable of hone in somewhat bit quicker.

I believe an enormous future step (that isn’t essentially within the close to future) is incorporating language. It’s very thrilling with how giant language fashions have gotten so a lot better, but additionally there’s a whole lot of fascinating questions. Up till now, I haven’t actually included pure language. A part of it’s as a result of I’m not totally positive the place it matches within the implicit versus express delineation. On the one hand, you may say “good job robotic”, however the best way you say it could imply various things – the tone is essential. For instance, in case you say it with a sarcastic tone, it doesn’t essentially imply that the robotic truly did an excellent job. So, language doesn’t match neatly into one of many buckets, and I’m excited about future work to suppose extra about that. I believe it’s an excellent wealthy house, and it’s a means for people to be rather more granular and particular of their suggestions in a pure means.

What was it that impressed you to enter this space then?

Truthfully, it was somewhat unintended. I studied math and laptop science in undergrad. After that, I labored in consulting for a few years after which within the public healthcare sector, for the Massachusetts Medicaid workplace. I made a decision I wished to return to academia and to get into AI. On the time, I wished to mix AI with healthcare, so I used to be initially excited about scientific machine studying. I’m at Yale, and there was just one particular person on the time doing that, so I used to be the remainder of the division after which I discovered Scaz (Brian Scassellati) who does a whole lot of work with robots for folks with autism and is now shifting extra into robots for folks with behavioral well being challenges, issues like dementia or anxiousness. I assumed his work was tremendous fascinating. I didn’t even understand that that form of work was an possibility. He was working with Marynel Vázquez, a professor at Yale who was additionally doing human-robot interplay. She didn’t have any healthcare initiatives, however I interviewed along with her and the questions that she was excited about have been precisely what I wished to work on. I additionally actually wished to work along with her. So, I by accident stumbled into it, however I really feel very grateful as a result of I believe it’s a means higher match for me than the scientific machine studying would have essentially been. It combines a whole lot of what I’m excited about, and I additionally really feel it permits me to flex backwards and forwards between the mathy, extra technical work, however then there’s additionally the human factor, which can also be tremendous fascinating and thrilling to me.

Have you ever bought any recommendation you’d give to somebody pondering of doing a PhD within the discipline? Your perspective will probably be notably fascinating since you’ve labored exterior of academia after which come again to begin your PhD.

One factor is that, I imply it’s form of cliche, however it’s not too late to begin. I used to be hesitant as a result of I’d been out of the sector for some time, however I believe if you will discover the fitting mentor, it may be a very good expertise. I believe the most important factor is discovering an excellent advisor who you suppose is engaged on fascinating questions, but additionally somebody that you just wish to study from. I really feel very fortunate with Marynel, she’s been a superb advisor. I’ve labored fairly carefully with Scaz as effectively they usually each foster this pleasure concerning the work, but additionally care about me as an individual. I’m not only a cog within the analysis machine.

The opposite factor I’d say is to discover a lab the place you will have flexibility in case your pursuits change, as a result of it’s a very long time to be engaged on a set of initiatives.

For our last query, have you ever bought an fascinating non-AI associated reality about you?

My principal summertime passion is taking part in golf. My complete household is into it – for my grandma’s one hundredth birthday celebration we had a household golf outing the place we had about 40 of us {golfing}. And truly, that summer season, when my grandma was 99, she had a par on one of many par threes – she’s my {golfing} function mannequin!

About Kate

Kate Candon is a PhD candidate at Yale College within the Laptop Science Division, suggested by Professor Marynel Vázquez. She research human-robot interplay, and is especially excited about enabling robots to higher study from pure human suggestions in order that they will turn into higher collaborators. She was chosen for the AAMAS Doctoral Consortium in 2023 and HRI Pioneers in 2024. Earlier than beginning in human-robot interplay, she acquired her B.S. in Arithmetic with Laptop Science from MIT after which labored in consulting and in authorities healthcare.