What a Cognitive Psychologist Learned From Robotics
By: You, Lilian
Tags: Robotics, NIPS, Human Behavior, Psychology, AI

I am a dog lover. My earliest impression on robotics comes from Sony’s Aibo dog. Later, I became a fan of Boston Dynamics’ Spot. Like it has only existed in many people’s imagination of the human future that one day we could have our own robot servers at home to help with doing all the boring labors: Robotics had been a word that’s so cool and quite distant from my life.

The first time I learned the ‘not so cool’ side of robotics is when I read a literature on path integration (simply put, how to locate oneself during movement) where it mentions that very simple human path integration tasks such as walking in a circle (technical terms: loop closure tasks, or more prevalently used triangle completion tasks) is really challenging for robots. That’s when I realized that as a cognitive psychologist who study human, I have been taking so many of the good human abilities for granted (e.g., senses, memory, learning, etc.). Whereas robots are just like newborn babies, the world is so complicated and strange to them so they will usually start with failing to do anything: The story behind a cool robot video is usually million times of trials and errors. I started to appreciate the delicately designed stable human system that both me and my human subjects owned.

This year, I was fortunate to attend the 3rd Robot Learning Workshop at NeurIPS 2020 thanks to the generous registration support from the workshop. I was highly motivated to attend this one-day-long workshop for the course I took this quarter on Cognitive Robotics. Professor Jeff Krichmar, who is a neuroscientist, and a roboticist, taught the course. In his course, I learned to implement neural networks on simulated robots to test on some neuroscience theories. To a cognitive neuroscientist who is struggling with human subject recruitment during the pandemic — being able to create own participants (virtual robots) and conduct experiments on them — is purely fascinating. I was eager to learn more. Therefore, when I heard about the opportunity to attend the robot workshop at NeurIPS, I did not hesitate to apply for it.

The day-long robotics learning workshop focused on Grounding Machine Learning Development in the Real World. To me, the most fruitful part of workshop is the discussion section where several roboticists discussed on some hardcore questions in robotics.

Screenshot of David and Teddy taken by Nathan Flemming from the movie A.I. (2001).

Interestingly, just like the pandemic pushes cognitive psychologists to move experiments online using remote testing platforms such as Amazon Mechanical Turk and Pavlovia, pandemic also pushes roboticists to move experiments from real-world physical experiments to robot simulation platforms such as Webots, RaiSim, and Isaac Gym. This brings up the big topic at the panel discussion on Sim2Real (apply what learned from robot simulation to real-world robots) problem. Here are some valid points I got from the discussion panel (courtesy of Prof. Peter Stone): On the one hand, simulators are never perfect enough to be directly applied to the real life, even if it is ground based. On the other hand, simulators are really necessary and useful. Just like Doctor Strange in the last episode of the Avengers who pre-played millions of solutions in mind trying to find an optimal action to defeat Thanos, simulators largely decreased trials and errors of robot experiments in the physical world by producing well pre-trained networks. Solutions to the Sim2Real problems include 1) Adding randomness to simulations so it can better represent the real-world situation, which is like doing counterbalance in psychology experimental design. 2) Incorporating real-world data and doing Sim2Real and Real2Sim iteratively. This is a superb point. I think the iterative ground-reground thinking can also apply to cognitive research in forming a loop between human subject experiments and computational cognitive modeling. In addition, similar ‘Sim2Real’ concern applied to research on humans. Traditional psychology experiments take place in a dark enclosed room. Well, as a human being, you know that feels different from your real-life scenario, don’t you? Thus, it’s always a concern that whether psychology studies confidently replicate human behaviors in real-world environments. Robotics, however, might give a new opportunity for psychologists to verify how different things actually work out by applying mechanisms we learned in humans to robots, and then observe robots’ real-world behaviors.

Dr. Carolina Parada from Google Robotics gave a nice landscape of the robotics in her talk. Critically, she mentioned the concept of two types of robots: robots used in industry and robots produced in industry. The former one is used by programmers, while the latter one is used by consumers. Often times, we are just thinking about the latter type: the human-centered robots. Just like many people are thinking about social psychology or psychological counseling when coming to psychology, which are really just two branches out of the gigantic tree of psychology.

Similar to appreciating the robustness of the human functioning system, learning about robotics gave me some new lens in understanding humans:

Think developmentally. As I’ve mentioned above, many robots need a learning process to gain an ability which is like human development. As far as I knew, most studies in cognitive psychology are cross-sectional. It is understandable because longitudinal studies really need to invest a tremendous amount of time and money. However, to understand (and in robotics ‘to create’) one ability we really need to add a time dimension to it. Without understanding its developmental history, it’s hard to say that we understand the underlying mechanisms of an ability confidently.

Think holistically. Simply learning the process of how to make a robot move even at a baby step really let me zoom out of the human head and think more about the entire human body functioning as a whole. Well, I cast no doubt on the importance of CNS (central nervous system) in controlling human behavior. However, to complete any tasks, we have to be a multi-tasker. To give you a simple example, to reach an apple on the desk, we have to first see (visual perception, visual recognition) and locate the apple (inertial metrics), then walk toward it (motor system for torso and legs), and finally extend one of our hands to grab the apple (a quick decision making of action selection, proprioception of different joints of the selected hand, and motor system for hands). How many senses and brain areas take part in this process? Many. Only understanding how visual dorsal stream work (the famous ‘where’ pathway in the brain), without a coordinated movement of different body parts, cannot give us the apple. By the way, I need to clarify that the ‘multi-tasker’ here refers to separate body mechanisms that contribute to one task, instead of the commonly said ‘different tasks’ that diverts your attention. I agree that we’d better focus on one task at a time to stay efficient.

Think cheaply. Unless a robot is charged 24/7, it can only do limited tasks before the energy runs out. That pushes roboticists to design the robot system smartly or say ‘cheaply’. Psychology studies, however, require human subjects to complete the same task hundreds of times in one experiment. And as I’ve heard, research on monkeys could require a monkey to complete a task thousands of times in one experiment which is equivalent to testing a human subject multiple times over weeks or even months. Well, in both cases, humans and monkeys were not kept ‘charged’ during the experiment. Of course, healthy humans’ and monkeys’ energy don’t run out that quickly, but is having an extensive number of repeated trials really a good way to study underlying mechanisms of cognitive abilities? I don’t know the answer. I may continue testing my human subjects in the traditional experimental psychology way, but how to do things ‘cheaply’ is definitely one thing I will keep thinking about.

Think tolerably. Robots make mistakes for different reasons. Sometimes a robot fails for a legitimate reason related to the system design, and that could be fixable. Other times the reason seems hard to tell. If you’ve ever programmed, you may know that sometimes one way to ‘debug’ is just to restart the software or restart the computer. How the ‘bug’ gets automatically fixed — someone may tell — I have no idea. It may not be a real ‘bug’. Would it be similar in humans? Maybe not all phenomena we occasionally observed in human behaviors will have an explanation or are necessary to require an explanation. There is also downside of over explaining. From a machine learning perspective, if we overfit human behaviors with branches of explanations, it may overshadow the trunk explanation and make it hard to transfer to understand other similar behaviors. Therefore, keeping some tolerance level for ‘behavioral errors’ in human observations may prevent us from being trapped by unnecessary details.

Although I may or may not go further into the robotics track of research in the future, it is definitely one of the coolest research areas I’ve learned during graduate school and will be keep paying attention to. It is also interesting that when learning about another subject really helps me reflect on my own subject.

Last to cat lovers, here is a social robot companion pet — Orange Tabby Cat — a joyful robot that you can give to your aging loved ones.