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

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

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

I examine human-robot interplay. Particularly I’m excited about how we will get robots to higher study from people in the way in which that they naturally educate. Sometimes, lots of work in robotic studying is with a human instructor who is barely tasked with giving specific suggestions to the robotic, however they’re not essentially engaged within the process. So, for instance, you may need a button for “good job” and “dangerous job”. However we all know that people give lots 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 facet {that a} robotic arms them – that’s implicitly saying that that was the improper factor handy 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 approach to get extra data with out including any burden to the human person. Previously, I’ve checked out these two streams (implicit and specific suggestions) individually, however my present and future analysis is about combining them collectively. Proper now, now we have a framework, which we’re engaged on enhancing, the place we will mix the implicit and specific suggestions.

When it comes to choosing 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 arduous to interpret implicit cues. Individuals will reply otherwise, from individual to individual, tradition to tradition, and so on. And so it’s arduous to know precisely which facial response means good versus which facial response means dangerous.

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 totally different motion areas, however we will discover an abstraction in order that we will know that if a human does an motion, what the same actions could be that the robotic can do. That’s the implicit suggestions proper now. After which, this summer season, we wish to lengthen that to utilizing visible cues and taking a look at facial reactions and gestures.

So what sort of eventualities 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 example as a result of it’s a setting the place it’s straightforward to think about why this stuff would matter. I additionally like that cooking has this ingredient of recipes and there’s a formulation, however there’s additionally room for private preferences. For instance, anyone likes to place their cheese on prime 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 will 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 yr is extending this to group interactions. So if now we have a number of folks, possibly the robotic can study not solely from the particular person reacting to the robotic, but in addition study from an individual reacting to a different particular person and extrapolating what which may imply for them within the collaboration.

Might you say a bit about how the work that you simply did earlier in your PhD has led you so far?

After I first began my PhD, I used to be actually excited about implicit suggestions. And I believed that I needed to deal with studying solely from implicit suggestions. One in every of my present lab mates was targeted on the EMPATHIC framework, and was trying into studying from implicit human suggestions, and I actually favored that work and thought it was the course that I needed 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 examine the place I had folks play a sport with a robotic. We recorded their face whereas they have been enjoying the sport, after which we tried to see if we might predict based mostly 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 enjoying 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 feel that is one thing that’s actually necessary 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 massive reactions however it wasn’t essentially to what the agent was doing, it was to one thing that that they had completed within the sport. For instance, there’s this clip that I all the time use in talks about this. This particular person’s enjoying and he or she has this actually noticeably confused, upset look. And so at first you would possibly suppose that’s destructive suggestions, regardless of the robotic did, the robotic shouldn’t have completed that. However if you happen to truly take 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 House Invaders, and he or she obtained hit by one of many aliens and her spaceship disappeared. And so based mostly on the context, when a human appears to be like 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 habits. I feel that was actually thrilling. After that, we realized that utilizing implicit suggestions solely was simply so arduous. That’s why I’ve taken this pivot, and now I’m extra excited about combining the implicit and specific suggestions collectively.

You talked about the express ingredient could be extra binary, like good suggestions, dangerous suggestions. Would the person-in-the-loop press a button or would the suggestions be given by way of speech?

Proper now we simply have a button for good job, dangerous job. In an HRI paper we checked out specific suggestions solely. We had the identical house invaders sport, however we had folks come into the lab and we had just a little Nao robotic, just a little humanoid robotic, sitting on the desk subsequent to them enjoying the sport. We made it in order that the particular person might give optimistic or destructive suggestions throughout the sport to the robotic in order that it might hopefully study higher serving to habits within the collaboration. However we discovered that folks wouldn’t truly give that a lot suggestions as a result of they have been targeted on simply attempting to play the sport.

And so on this work we checked out whether or not there are other ways we will remind the particular person to present 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 if you happen to’re distracting them. And likewise you don’t essentially all the time need suggestions, you simply need it at helpful factors. The 2 circumstances we checked out have been: 1) ought to the robotic remind somebody to present suggestions earlier than or after they struggle a brand new habits? 2) ought to they use an “I” versus “we” framing? For instance, “bear in mind to present suggestions so I could be a higher teammate” versus “bear in mind to present suggestions so we could be a higher crew”, 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 specific suggestions, however we wish to see now if we mix that with a response from somebody, possibly that time could be a superb time to ask for that specific suggestions.

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

The large factor motivating lots of my work is that I wish to make it simpler for robots to adapt to people with these subjective preferences. I feel by way of goal issues, like having the ability to choose one thing up and transfer it from right here to right here, we’ll get to some extent the place robots are fairly good. Nevertheless it’s these subjective preferences which might be thrilling. For instance, I like to cook dinner, and so I would like the robotic to not do an excessive amount of, simply to possibly do my dishes while I’m cooking. However somebody who hates to cook dinner would possibly need the robotic to do the entire cooking. These are issues that, even when you’ve got the right robotic, it might’t essentially know these issues. And so it has to have the ability to adapt. And lots of the present desire studying work is so knowledge hungry that you must 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 life like for folks to truly have a robotic within the dwelling. 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 specific and implicit suggestions will assist or not it’s extra naturalistic. You don’t need to essentially know precisely the precise approach to give specific suggestions to get the robotic to do what you need it to do. Hopefully by way of all of those totally different alerts, the robotic will have the ability to hone in just a little bit sooner.

I feel a giant future step (that’s not essentially within the close to future) is incorporating language. It’s very thrilling with how massive language fashions have gotten so significantly better, but in addition there’s lots 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 absolutely certain the place it matches within the implicit versus specific delineation. On the one hand, you possibly can say “good job robotic”, however the way in which you say it might imply various things – the tone is essential. For instance, if you happen to say it with a sarcastic tone, it doesn’t essentially imply that the robotic truly did a superb 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 feel it’s a brilliant wealthy house, and it’s a approach for people to be far more granular and particular of their suggestions in a pure approach.

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

Actually, it was just a little unintentional. 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 needed to return to academia and to get into AI. On the time, I needed to mix AI with healthcare, so I used to be initially serious about medical machine studying. I’m at Yale, and there was just one particular person on the time doing that, so I used to be taking a look at the remainder of the division after which I discovered Scaz (Brian Scassellati) who does lots 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 nervousness. I believed his work was tremendous fascinating. I didn’t even notice 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 together with her and the questions that she was serious about have been precisely what I needed to work on. I additionally actually needed to work together with her. So, I by chance stumbled into it, however I really feel very grateful as a result of I feel it’s a approach higher match for me than the medical machine studying would have essentially been. It combines lots of what I’m excited about, and I additionally really feel it permits me to flex forwards and backwards between the mathy, extra technical work, however then there’s additionally the human ingredient, which can also be tremendous fascinating and thrilling to me.

Have you ever obtained any recommendation you’d give to somebody considering of doing a PhD within the area? Your perspective will probably be notably fascinating since you’ve labored outdoors of academia after which come again to start out your PhD.

One factor is that, I imply it’s form of cliche, however it’s not too late to start out. I used to be hesitant as a result of I’d been out of the sphere for some time, however I feel if you could find the precise mentor, it may be a very good expertise. I feel the most important factor is discovering a superb advisor who you suppose is engaged on fascinating questions, but in addition somebody that you simply wish to study from. I really feel very fortunate with Marynel, she’s been a wonderful advisor. I’ve labored fairly carefully with Scaz as effectively and so they each foster this pleasure concerning the work, but in addition 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’ve 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 closing query, have you ever obtained an fascinating non-AI associated truth about you?

My predominant summertime interest is enjoying golf. My complete household is into it – for my grandma’s a centesimal 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} position 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’ll 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.