A Summer for Path Planning Lessons: for Robots & REU Student Alike

Established as part of the National Science Foundation (NSF) Research Experiences for Undergraduates (REU) Summer Undergraduate Research in Engineering (SURE) grant, the SURE Robotics Program adds a robotics component to Georgia Tech’s SURE Program, established in 1992.

Launched in May 2014, and funded with co-support from the Department of Defense and NSF’s Division of Engineering Education and Centers, the program supports undergraduate students in an immersive, ten-week summer robotics research experience designed to attract qualified underrepresented students into graduate school in the fields of engineering and science.  In 2021, the in-person GT SURE Robotics program was hosted by the Woodruff School of Mechanical Engineering with support from IRIM.

Over the months of the 2021 Fall Semester, IRIM will be highlighting each of the  undergraduate participants, their research topics and experience in the labs, as well as what they gained from the program and their time at Georgia Tech, and in Atlanta. Our first interviewee from the program is Mark Jimenez, an Incoming Senior in Computer Science at the University of Hawai`I at Hilo.

Name: Mark Jimenez
PI: Dr. Anirban Mazumdar 
Mentor: Hayden Nichols

1. What sparked your interest in robotics and what problems are you hoping to help solve as a roboticist?

My interest in robotics stems from my interest in Artificial Intelligence (AI). While following my interest in AI and computing, I found that many difficult and important problems in robotics are in the field of autonomy, and AI is looked to as a key in much of the research in this field. I personally believe that robotics is one of the most obvious and direct ways in which AI can benefit humans and our society. From caretakers and autonomous vehicles to household and industrial robots, AI and robotics must be paired in order to take on complex problems. More importantly than simply teaching robots to do work that humans can, I see robotics as expanding access to critical services such as healthcare, transportation, food distribution, and emergency services. Moreover, I believe that robotics can and should be used to make the lives of working people simpler and safer, by increasing the use and efficacy of reliable autonomous systems in factories, small businesses, and homes. Personally, I hope to help solve the problem of robot education via "self-direction" advancing robotic learning abilities so that our robots are malleable enough to know how to teach themselves new skills, rather than needing to be reprogrammed every time a new task presents itself.

2. What research are you conducting at GT and what applications do you feel this research may have?

At GT, I am working on mathematically based robotic path planning (A to B) and classification (of the robot's surrounding environment). There are many obvious applications of path planning, and planning algorithms have been implemented extensively in real‐world robotic systems, from welding robots to self‐driving cars. I find the classification aspect of this project to be particularly intriguing, as many robots currently in use are completely unaware of their environmental surroundings, outside of their programmed task. Having robots that can anticipate the movement of other agents in their environment is an important step in expanding the use of robots in household and "human" environments such as hospitals and care facilities, or hazardous search and rescue tasks. Our project examines both path planners and observers.  The planners are creating trajectories and the observers are trying to understand the planner behavior. This relationship can be symbiotic robots predicting each other's paths and future positions could be a critical aspect of many cooperative robotic tasks. For example, imagine a cooking and cleaning robotic pair, explicit cooperative planning may not be feasible, and so these robots would need to accomplish their tasks without interfering with one another’s work.

3. What has been your favorite academic lab activity/ tool training/ etc. thus far and why?

Being able to build multiple neural networks, each individually capable of time series classification in order to solve an important problem, has been my favorite activity. I have worked with neural networks in the past, but building a network from scratch and at a low level, using TensorFlow and not some higher level API has been a tremendously valuable learning experience. Writing at this lower level has allowed me to understand more fully what is happening, programmatically, during the training of a neural network. Moreover, having access to the underlying operations taking place during training and prediction allows me to create highly customizable networks and layers in future research.

4. Do you feel this REU experience has helped prepare you for working in a collaborative laboratory environment and furthered your education goals?

This REU has undoubtedly prepared me for working in a collaborative research environment; I have been able to cooperate with both master’s and Ph.D. students, as well as a professor/lab director, so I have been working alongside every major academic role in a university lab. More importantly, I have been able to learn valuable insights and skills from each of these academics, and I have a much better understanding of how high‐level research laboratories function and produce work. This has unquestionably furthered my academic goals, as I have been able to work full‐time in a research setting on an incredibly interesting project with many avenues for exploration, which is exactly what I want to do.

5. What are your plans post-undergraduate?

After graduating from my current program in Computer Science, I intend to obtain a Ph.D. in Computer Science, with a focus in Artificial Intelligence. To me, robotics is one of the most exciting aspects of Artificial Intelligence, and robot learning is something of paramount interest to me. Moreover, the history of AI research in robotics has shown how difficult and data‐intensive robot learning is, meaning that there is considerable work and progress to be made in this field. I believe AI has tremendous implications for robotics, and robotics is the field in which AI can have an immediate and profound impact on our society and in our day to day lives, which is exactly what excites and drives me to pursue a Ph.D. in AI.

6. What is/was your favorite thing about/impression of GA Tech and ATL?

GT has a beautifully green campus, exceptional facilities, innovative ideas, and hard‐working, passionate researchers. Specifically in my lab, the students here have been extremely welcoming and helpful, with genuine passion and excitement for their research. It has been a joy to come into work each day and work alongside such enthusiastic and talented individuals. Atlanta is likewise a beautiful setting for research, with comfortable weather, rich natural beauty, and an original, diverse culture.