Category: Robotics

Making Robotics a Popular Hobby?

by Yu Gu, posted in Future, Ideas, Robotics

Photography, electronics, ham radio, model airplane, 3D printing, astronomy, …, there are many science and engineering related hobbies. Most of these hobbies have their dedicated forums, magazines, trade shows, and competitions. Behind each of these hobbies, there is an ecosystem of companies, large and small, making general or specialized equipment and software.

Robotics as a hobby? It’s starting to be a thing now, but not quite comparable to the established ones yet. There are several organized robotics events, like FIRST, involving tens thousands of kids. But a hobby is something more personal and spontaneous… Where are the robotics hobbyist forums? magazines? organizations, trade shows? competitions? To be fair, some of them are popping up, but the reach has been limited.

What makes a hobby popular? In my opinion, based on experiences with my two hobbies, amateur astronomy and photography, several factors are important. First, it needs to be intriguing. Second, it needs to have a low entry barrier: e.g., a kid with no one around to help can get started and accomplish something, enough to sustain the interest. Third, it needs to have no upper bound in terms of what can be achieved; e.g., room for fiddling, imagination, and creativity. Anyone in a hobby would know that it’s an endless endeavor to complexity and perfection. Afterall, a hobby is a form of obsession. Finally, the connections between the easy (entry) parts and the hard (advanced) parts need to be there.

I think robotics is intriguing for an enough number of people. It has the room for people to show their talent and creativity, in limitless ways. Getting a low-end robot kid is also not much more expensive than a low-end camera/radio/telescope; maybe a bit harder to use at first. So what is the problem? maybe it’s in the connections. What would you do once you are ready to move up from your Lego Mindstorms? Do you have to discard all the kit you have and start over with a new system? Is the knowledge you gained with a Lego robot transferable to a robot based on Raspberry Pi? What if you want to add soft manipulator, mapping, and natural language processing to your hobby robot? There are open-sourced ways to do that, but not obvious to most people.

Of course, a hobby does not have to be easy, and we don’t want it to be easy as well. Most hobby involves hard problems. For example, few amateur astronomers know how to grind a mirror (although that was how astronomy became popular during the Great Depression) and even fewer know how to make a lens. However, imaging near telescopes’ diffraction limit, discovering exoplanets, amateur astronomers are making great contributions to both engineering and science.

Amateur Roboticists can be this successful too. Of course, the growth of a hobby is an emergent behavior, depending on many factors such as people’s influence on each other. Here, I have a few ideas that may help improve the connections between “entry-level” and “advanced” activities. First, coming up with standardization of key robot components (e.g., hardware and software interfaces) so multiple companies and amateurs can contribute to their developments. Second, making ROS (Robot Operating System), an already successful middleware platform to researchers, accessible to high-school students and hobbyists, through easier interface and readable documentation, and provide demos on common platforms (e.g., robots developed for FIRST and VEX competitions). Three, leveraging the 3D simulation capabilities and people’s interest in gaming to develop open-source robot simulations for the hobbyist community.

Once robotics becomes a popular hobby, companies would make more money, hobbyists would have more toys, researchers would have more helpers, the acceptance of robotics would improve, and the field of robotics would advance faster!

P.S. a key difference between professionals and hobbyists: professionals get paychecks to do certain things. Hobbyists spend their paycheck to do the same things. You can probably tell that the hobbyists are often more motivated…

The Future of Remote Work? Maybe Humanoid Telepresence Robots Can Help

by Yu Gu, posted in Future, Ideas, Research, Robotics

Tired of being stuck at home working alone remotely? You are not alone in that sense! Since COVID-19 turned the world upside down, many of us were forced to work from home. After a while, once we get used to it, working from home, or working remotely, is actually not all that bad. We can spend less time in the traffic and enjoy more flexibility sometimes. But there are important things missing, like face-to-face discussions and the ability to modify the environment (many jobs depend on these!). Or in short, we are missing out on the social and physical interactions.

Will remote work be the same as what we are doing today (e.g., meeting on Zoom), say, 20 years from now? Hopefully not…, OF COURSE NOT! So what may change?

Let’s envision a future hybrid workplace, for example, an office with local workers and a group of telepresence humanoid robots as “avatars” of remote workers. Whenever a remote worker needs to do something beyond a computer task, for example helping a customer or turning a knob, she/he may do so through one of the robots. The humanoids have articulated arms and bodies to support human-like interactions, e.g., during a “face-to-face” conversation. When an office worker or a customer puts on a pair of Augmented Reality glasses, the live image of the remote person would be overlaid over the robot. At homes, the workers also feel they are physically experiencing the remote work environment, instead of feeling isolated.

My student Trevor Smith created this illustration in Gazebo using images of a VR treadmill the robot Pepper.

Of course, a lot of research needs to be done for this dream to become a reality, but that’s what we roboticists are here for. Communication technology has allowed us to hear from a distance, then to see each other, maybe this time we would finally get to “travel, touch, feel, and experience” through internet and robots? Sounds farfetched but not impossible.

A 2019 MIT report on the Work of the Future pointed out that “Ironically, digitalization has had the smallest impact on the tasks of workers in low-paid manual and service jobs. Those positions demand physical dexterity, visual recognition, face-to-face communications, and situational adaptability. Such abilities remain largely out of reach of current hardware and software but are readily accomplished by adults with moderate levels of education.” By focusing on labor-complementing instead of labor-substituting technology development, improving remote work may be a way of using robotics and AI to support middle-class workers (e.g., teachers, social workers, farmers, and factory workers) of the future.

An NSF REU Proposal on Human-Swarm Interaction

by Yu Gu, posted in Research, Robotics, Students, Uncategorized

I am sharing a REU (Research Experience for Undergraduates) proposal. When I first started writing this proposal, along with my colleagues Dr. Gross and Dr. Klink, we had no idea where to start. I searched the internet and found a couple sample REU proposals. Several friends also kindly shared their successful proposals. Now, it’s my turn to give back!

The project is about human-swarm interaction: how can one person effectively manage a 50-robot swarm in achieving a high-level goal? In the project, the undergraduate students need to design the robots, the test environment, a simulator, human-machine interfaces, along with swarm control algorithms. They would also perform a variety of experiments to demonstrate these capabilities.

During the first year (2019) program, eight undergraduate students from around the country worked together with several WVU students for 10 weeks. They designed and built 50 robots (!) and developed the basic swarm control software. Here is a video:

Here is a paper written by the students. One major difference between our REU Site and most other REU programs is that all students were working together on a same project. They had to work as a team (I called them a swarm…) to achieve the overall project goal.

This year (2020), however, we had to cancel the program due to COVID. We will be back in motion again next year!

The proposal received a C (Competitive) from the NSF review panel. The individual reviewer ratings were VG/G, VG, VG, VG/G (VG – very good, G – good). In general, the reviewers were excited about the project research ideas, but had concerns about our detailed program designs. Of course, we were very inexperienced in this area at the time.

Attachment: REU Proposal

The CV that Gets You the Faculty Interviews

by Yu Gu, posted in Faculty, Job, Robotics, Students, Uncategorized

If you are a Ph.D. student or a post-doc, you probably have thought about the prospect of becoming a professor at some point, even though this option may not be high on your list. If you only thought about getting a faculty position shortly before applying, you probably won’t get an interview; because it takes planning and time to build up a CV that is competitive for the faculty job market. When is the best time to start planning your academic CV? During your undergraduate years. Ok, if you missed that, and I don’t think I know anyone who didn’t, the earlier the better. I only started caring about my CV three years after my Ph.D., and I had to pay for that …

A strong CV is simply the most important part of your faculty job application package, and sometimes, it may be the only thing that a search committee member reads.

Sun Tzu once said: “If you know the enemy and know yourself, you need not fear the result of a hundred battles.”

There would probably be more than 100 battles for a faculty-job applicant. Let’s first think about your opponents: the search committee members. We are talking about a bunch of professors who may or may not be in your field. They have all failed numerous times in the past but those memories have faded. They are now trying to get through busy daily schedules and fulfill the service duty with the minimum time/energy costs. Reading tens, if not hundreds, of applications is just not that fun for them anymore. To prevent these people from making arbitrary decisions there is likely some kind of forms that the search committee members must fill. These are probably pre-determined performance metrics that evaluate your record or potential on teaching, research, and service. What does all this mean? It means your CV needs to be simple (easy to find relevant information) and well rounded. You shouldn’t be lagging your competitors in any of the major categories (e.g., publication, teaching, funding, and service). An easy way to find out the going credentials is to check out the CVs of newly hired faculty members in your field; they are not hard to find on the internet. Scoring a zero in any of these categories is not going to look good for you.

You might wonder how would you get teaching and funding experiences when you are still a graduate student? You can if you try hard enough. For example, you can offer to teach or co-teach a class; you can submit fellowship proposals; and you can attend teaching and grant writing workshops. A reasonable search committee would consider your available opportunities as a student, but you must demonstrate that you are proactive and have the potential. This would also help you later during the interview process.

Would this be enough? Not yet. Having all the boxes checked may get you a decent first impression. It may also take away some of the arguments for someone on the committee to strongly against you. What you really need now is a few strong supporters on the committee. In other words, you must impress them.

What makes your CV look impressive? Here is my rule of thumb: the most impressive things are the ones many people have tried but could not get. If you have done something unique but no one on the search committee has thought about trying, then sadly, it’s likely to go unnoticed. What are some examples of impressive things? publications in highly selective journals/conferences, best paper awards, prestigious fellowships/grants, a new theory, solving a known hard problem, …. Of course, you need to be good, work hard, and have abundance of luck, to get even one of these. Many people focus energy on achieving the seemingly least uncertain goal: increase the number of high-quality publications. However, if you have special talents, you may find a different path to success.

I want to quickly bring up the discussion of quality vs quantity here. I have seen CVs with over 100 papers not being appreciated by the search committee. A CV with just 1-2 good papers (unless they are on Nature or Science…) may also not be convincing enough. What if this person was just lucky or was working in a very small niche area? In robotics, for a freshly graduated Ph.D., I think 2-3 first-authored top journal papers and 2-3 first-authored top conference papers, plus a similar number of non-first-authored high-quality papers would make your CV look quite impressive. It’s certainly not easily achievable within the Ph.D. period. Adding more low-quality papers to the mix would only serve to reduce the perceived quality of your CV.

OK, say you have gotten a decent impression for your application package and the committee will meet next Wednesday to decide on a list of candidates to be interviewed. By the time the committee meets, the contents in many applications may have gotten mixed up in their heads, because they all look so much alike… The only chance you have is if you got something stands out, pointed out by one of your advocates on the committee. Other members would quickly flip to that page on your CV. If they also agree and cannot remember anything bad to say about you, then congratulations, most likely you would get an interview.

So, let me summarize it: create a CV that maximize the “expected” number of supporters (e.g., having something impressive) and minimize the number of naysayers (e.g., being well-rounded).

How to get there? This is the part about “knowing yourself.” Discover your true passions and strengths and come up with a plan for yourself. Well, …, that’s easy to say than done. Without known better, one way that worked well for me was to start by making myself look bad: creating the Google Scholar profile and making it public; posting the CV online; asking myself awkward questions like what are my top 3 contributions to the field? I found things usually get better after I learned to not avoid myself.

Attachment: my CV back in 2008, over 3 years after my Ph.D., which, unsurprisingly, did not get me anywhere.

So, What Do We Do with It?

by Yu Gu, posted in Astronomy, Photography, Research, Robotics, Uncategorized

I am more of a telescope collector than a sky watcher. I have about a dozen telescopes of different designs: achromatic, apochromatic, doublet refractors, triplets, Newtonians, Maksutov-Cassegrain, Maksutov Newtonian, H-alpha solar scope, Dobsonian mounts, German equatorial mounts, roof prism binoculars, porro prism binoculars… you name it. I know way more about telescope designs than constellations of the sky or features on Mars. Most of my telescopes spend years collecting photons in a very dark place: my closet.

I am more of a camera lover than a photophile. I have several cameras from the film era to the mirrorless age. I have a couple dozen lenses with focal lengths ranging from 14mm-500mm, not counting telescopes.

I have learned to accept this. There is nothing wrong with being obsessed with equipment, I told myself, the hobby is supposed to be fun!

I also like robots. My lab, IRL, has about two dozen robots, plus a 50-robot swarm. The UAV lab I worked in before had about a dozen UAVs. Most (but not all) of these robots and UAVs were custom developed. I, as someone who always like toys, had a hand in the design of most of these systems.

So now, what happens when we have all the hardware we ever wanted? Of course, we can only get close, but not there. There is a pretty big difference between “wants” and “needs”, and we often rationalize “wants” as “needs”. As engineers and perfectionists, seeing small issues with the current setup makes us feel itching. We are constantly dreaming up next design iterations. We are telling ourselves better robots will make our research better.

Is that true? Do we really need more/better robots to do better research? Maybe to some degree. If we don’t have the appropriate tools, we can’t do certain experiments. If we don’t have high quality equipment, some work may be very hard to do (e.g., mapping without 3D Lidar or robotic pollination without a precision manipulator). I think another important reason for having the best robots, like having the best telescopes/cameras, is that we have no one else but ourselves to blame for the underperformance…

So, let me ask again, what happens when we have all the hardware we ever wanted? What do we do with it? The answer is simple: let’s focus on research. Instead of rushing to start on the next generation design and letting the existing robots collect dusts, let’s make them do things nobody else can dream of or believe!

What Makes a Good Grand Challenge?

by Yu Gu, posted in Ideas, Robotics, Uncategorized

I am a big fan of Grand Challenges.

I was super motivated when reading about John Harrison, a self-taught engineer (carpenter) in the 18th century, who won the longitude reward through decades of perfecting clockmaking skills.  I also watched several DARPA Challenges with great interests. I have participated in, for three years, NASA’s Centennial Challenge on Sample Return Robot. Those three years left me with countless memorable moments to be enjoyed for the rest of my life.

What I like most about Grand Challenges is that they give people excitement and hope. Grand Challenges allow someone, who otherwise would not be known by people, such as John Harrison and Charles Lindbergh, to shine through their courage, dedication, and talent. They also can accelerate technology development, by bringing together a broader range of conventional and unconventional innovators and solutions.

However, many Grand Challenges failed to achieve these effects, for a variety of reasons. Here are a few factors I think maybe worth considering when designing a new Challenge.

  1. It needs to be relevant. If a Challenge addresses one of humanity’s most urgent needs, more people would likely to follow and participate.
  2. It must be a Challenge. A Grand Challenge needs to be hard. It should be a jump from any of our known abilities. It may sound impossible at first, but It’s so cool that it makes people imagine. The Challenge shall also not be too big a jump, otherwise everyone would fail (which is an acceptable but not desirable outcome).
  3. The Challenge description must be clear, rigorous, and stable. Like any games, there should be no ambiguity and room for interpretation. The actual tests must also precisely match the description. Unfortunately, quite often, the organizers did not fully think through all the issues at the beginning. They would come up with a set of rules that cause confusions (and potentially unfairness) and then they dumb down the challenge after most participants failed (this happens more often than you may want to believe!).
  4. Human factors must be kept at a minimum. One of the Grand Challenge’s greatest strengths is that it gives everyone a fair chance. You do not have to be a world renown thinker/scientist/engineer, you do not have to be rich, you do not even need to have a stable job; as long as you have a good idea, the skills, and the will (easy to say than done), you have your fair chance of winning. The success of a Grand Challenge should be defined by beating the problem, not anyone or anything else. If we allowed humans (e.g., the Challenge organizers) to pick winners based on their pre-conceived ways of solving the problem, John Harrison would had no chance against big name astronomers at the time (note: the Longitude Board, including Newton’s preference on finding an astronomy-based solution did cause hardship to Harrison for many years…). Let the results speak for themselves!
  5. Teams shall come up with their own resources, at least initially. This one may sound strange to you. Would it not be rewarding people with deeper pockets and leave the poor guys out of the fight? It might, but let’s consider the alternatives for a moment. What if the organizer picks a few promising teams, give each of them a few $M, so they don’t have to be sidetracked by fund raising and other resource constraints?  The question would then be: based on what criteria? prestige? track-record? how likely a team’s idea may work? If you read the history of Grand Challenges, you would know that none of these are reliable indicators of success. What this funding approach does is effective disincentivize the selected teams to push envelopes hard (they already have the cake; the final Challenge prize is just the icing) and block out all other competitors. In my opinion, just like any startups, each team needs to fight for its own survival the entire time. If you think you have a good idea, try to convince someone to fund you, or join another team with adequate resources. I think the phased approach being used by NASA Centennial Challenges is very good. Let teams compete for some initial phases (e.g., a simplified Challenge with a low-entry barrier) on their own dime, provide teams some funds once passed the initial phase. This record of success also helps teams to raise more funds from other sources.
  6. Give it a longer time frame. Most government funding mechanism have a short time horizon, but that is not necessarily good for getting the best outcomes. If a problem is of such importance to the society (e.g., determining longitude), why not keep the challenge alive for decades until it’s solved (luckily, it was!)? Short term focus leads to more applied solutions, discourages risky/crazy ideas, and more likely leads to the picking of lower-hanging fruits. Grand challenges for picking lower-hanging fruits? Does not sound good!
  7. Follow up after the challenge. Don’t let the whole thing ends the moment a victory is declared. Each participant probably has developed something unique/valuable; creating mechanisms (with funding) to support them working together for a little while may spark more innovations.

Of course, we all live within the real-world constraints. I will continue to be excited whenever a new Challenge is announced!

Engineering, Science, and Engineering Science

by Yu Gu, posted in Ideas, Robotics, Uncategorized

When I was a kid, my dream was to become a scientist. I was fascinated with reading early discoveries in chemistry, physics, and biology. Those scientists were my heroes.

Following a series of random and not so random events in life, I end up being an engineer, which I am equally happy about.

For a long time, I didn’t see much difference between science and engineering. We are all researchers. That was until I had my first proposal rejected by NSF, the National Science Foundation.

Scientists and engineers have different goals. Scientists discover and engineers create. Scientists observe something already exists (e.g., nature, universe, human society) and try to explain it. Engineers dream up something new (e.g., a bridge, a rocket, a material) and try to make it real.

For these reasons, scientists and engineers think and work in nearly opposite ways. Scientists observe, ask questions, form hypothesis, and then design experiments to test them. Engineers conceive designs, build prototypes, integrate parts into a system, and then perform evaluation.

If you ask an engineer to tackle a science problem, say why migrant birds often fly in formation; she/he may say let’s make airplanes fly in formation first, take measurements, and see what the data tell us. That was the kind of the mistake I made in writing my first NSF proposal.

Can someone function with both scientist’s and engineer’s minds? It’s very difficult. If you have sat in a meeting with both scientists and engineers, you would know that they don’t really speak the same language or live on the same planet (imaging adding a few artists into the mix!). Heck, they don’t even look the same. But the ability to handle difficulty is what sets one apart from the rest. I don’t have enough knowledge to comment on the importance of engineering to science, but in my opinion, it’s unlikely someone can be a great engineering researcher without sometimes thinking like a scientist.

Take robotics for example, we can always dream up robots that are more refined and algorithms that can squeeze out a few percentages of performance gain. In fact, we always want to do that because as engineers we feel itchy about flaws we can see, and improvements not made. Most of us live comfortably (or not so comfortably) in the cocoons we carefully engineered for ourselves. Every piece of silk we lay makes our world smaller. We are occupied and always so busy; while in the meantime, we ask, why innovation is so hard?

If only we could use some of our silk to explore, to take us to the next tree, and help us see a different world! What about taking a break from solving problems; spend some time to observe, ask why instead of how? We would be thinking like a scientist with the creativity and hands of engineers.

Of course, it would take a risky and painful transition to break the cocoon. But there is also no reason this cannot be done. Someone clearly had the wisdom at WVU a long time ago. After all, my office is in the Engineering Science Building (ESB).

Seventeen Mistakes that You Can Make in Writing Your First Paper

by Yu Gu, posted in Robotics, Students

Writing the first technical paper is hard. There are so many things that you want to write, and there are only a few pages that you are allowed to use. Once everything is put together, the manuscript does not read like any other paper that you have read. What went wrong?

Helping someone to write his/her first paper is also hard. I had my fair share of struggle with first time writers, whether English was their first language or not. The good news is that paper writing is not rocket science, everyone would eventually get it, sooner or later. To make this process a little less painful, mostly on my side :O), here is a checklist for you to use to avoid some commonly made mistakes:

  1. Not following the standard technical paper structure. There is no real need to innovate on the structure of the paper. Typically, we follow this order for a robotics paper: introduction/related work/contribution statement, problem statement, algorithm, experimental setup, results/discussion, conclusion and future work. The “related work” can be presented in the introduction section, as a separate section after the introduction, or near the end of the paper (seems to be a new fashion);
  2. Not paying attention to logic. Make sure to carefully design the paper before start writing. Every paper needs to have its overall purpose, theme, and flow. Every paragraph or sentence also needs to be logically connected to the ones before and after it;
  3. Not giving a clear and compelling argument at the very beginning of the paper.  Clearly identifying the objective in the first few sentences would reduce guesswork for the reviewers and channel their thinking to be aligned to what you want them to think. Pointing out the research gap and the potential impact would allow reviewers better appreciate the presented work;
  4. Not clearly framing the work in the literature. The main purpose behind the literature review section are: 1) to find the gaps/needs in the previous works to help identify the contribution of this paper; 2) to show that this work is built upon understanding of the history and the state-of-the-art (instead of coming out of thin air);
  5. Not clearly pointing out the innovation/contributions. Reviewers would be looking for this to make a decision, so try to help them out!
  6. Mixing problems with solutions in the problem statement section. This can have two negative effects: 1) it causes confusion and makes it harder to present the actual solution later; 2) it reduces the possible solutions to the problem and limits the imagination of the reviewer while reading the paper;
  7. Giving away the answer too early/easily. This is related to the previous point. Most of the time, if you present a solution right after stating the problem, people would trivialize it. This is like a movie spoiler. Present the problem, point out the challenges, let the reader fully appreciate the magnitude of the problem, then enlighten them. Writing a paper needs some serious storytelling skill!
  8. Not detailed enough so that the reader can reproduce the research or/and not concise enough so that the reader won’t get bored;
  9. The reasoning/argument is not bulletproof. Every statement needs to be backed up by facts and sound logic;
  10. Not providing proper evaluation of the results (e.g., metrics, statistics) and not providing confidence that this is a reproducible result. Spend some time to learn how to design experiments properly;
  11. Not providing insightful discussions of the results. Don’t just state the obvious!
  12. Not properly recognizing the limitations of the research. Every work has limitations. Typically this can be discussed in the assumptions (in the problem statement section) and in the future work section at the end;
  13. Not providing enough diagrams in the manuscript. A picture sometimes worth a thousand words. Try to have at least one figure, diagram, or table in each page. Attaching a cool video with the paper would also be important (especially for a robotics paper);
  14. Not fully describing every symbol used in equations;
  15. Not following the required format specified by the publisher;
  16. Having typos, grammatical errors, and formatting inconsistency;
  17. Forgetting to acknowledge sponsors, donors, other non-coauthor contributors.

Note that one of the most important parts of any form of communication is to understand the audience. For the papers, the reviewers are your peers but not necessarily very familiar with the specific problem that you are addressing. Their job is to make a binary classification (often) using the shortest amount of time (sort of like speed dating). Our job is to fight a series of cognitive biases of the reviewers. For example, it’s not uncommon for a reviewer to make a quick judgement after reading only the first few paragraphs (decisions based on incomplete information and heuristics). Once this judgement if formed, it’s difficult to change it. This is because they would be (subconsciously) looking for evidence to reinforce what they believed (confirmation bias). Now you see why the first sentence, the first paragraph, and the first page are so crucial to the success of a paper. Another human bias (availability heuristic) is that people value the most recent information more. This means you need to remind the reviewers all the good stuff about your paper at the end of it…

Well, so far we only talked about the organization aspect of paper writing. How to make sure what you published is not junk (instead, something that would make a technical impact to the community)? That’s another important topic, which we shall discuss at another time.

Looking for Talented and Motivated Students to Join IRL

by Yu Gu, posted in Job, Robotics, Students

My research lab is called the Interactive Robotics Laboratory (IRL). We are a group of creatively minded people that includes 10 graduate students, about a dozen undergraduates, and of course, me. Although we were only founded since 2012, IRL has made its name known in the robotics community. Our coming out party was the winning of NASA’s Sample Return Robot Centennial Challenge (total prize of $855,000). We were also the first group that developed precision autonomous pollination robot (just in case we won’t have enough bees in the future!). We are currently working closely with NASA’s Jet Propulsion Lab (JPL) in improving the autonomy of future Mars rovers (with seven students working for 10-weeks each at JPL).

We are also interested in the interactions among multiple robots. We are currently working on a cooperative UAV and UGV group in exploring underground tunnels. One of our bio-inspired ideas was recently selected by the highly prestigious NASA NIAC program. We are planning to send 100,000 micro ballooning spider probes into Mars’s global dust storm. With the funding form NSF, we are also working on human-swarm interaction: how can one human operator influence the global emergent behavior of a 50-robot swarm without directly controlling individual robots?

Our creative and futuristic work has drawn frequent media attentions. Our research was featured in over 65 news stories by media outlets such as the Discovery Channel, Wired, NASA 360, ABC News, Time Warner Cable, Associate Press, Aviation Week, and Air & Space Smithsonian Magazine.

Our success was building upon IRL member’s creativity, hard work, and ambitions. IRL provides a free thinking and collaborative environment that allow everyone to reach his/her full potential. We are also blessed for having the state-of-the-art facilities with more robots than human group members. At IRL, students are encouraged to develop their own research ideas, supported by resource provided internally and from external sponsors.

We are always looking for talented and motivated students to join us. Email me (yu.gu@mail.wvu.edu) if you are interested. As a diverse group, we are not looking for people of any particular background. For example, practical engineers and abstract thinkers are both appreciated in our group. Your GPA and GRE are also not as important as demonstrated ability to innovate and the obsessions towards creation (e.g.,  success in a related hobby). So make a case for yourself before emailing me.

Ph.D. Positions
We are always looking to fill 1-2 Ph.D. positions each year. These positions are fully funded (with tuition waived) by research projects or fellowships. To qualify, you should have excellent verbal and writing skills in English, should be motivated and capable of creatively working in a team environment. You can be enrolled in either the Department of Mechanical and Aerospace Engineering (for a ME or AE degree) or Lane Department of Computer Science and Electrical Engineering (for a CS, CpE, or EE degree). Please read the IRL guide for Ph.D. Study before applying.

M.S. Positions
IRL in general does not provide research assistantships to M.S. Students. If you are interested in pursuing a M.S. degree at IRL, you need to prepare your own funding or obtain an assistantship from the departments or WVU. IRL can assist perspective students with an outstanding credential to apply for Teaching Assistantships or Fellowships.

Undergraduate Research Positions
If you are currently a WVU undergraduate student who is interested in gaining research experience in robotics, you can participate in many different ways. For example, you can pursue a thesis or research credits at IRL; you can perform a senior deign project at IRL, or you can work as either a volunteer or hourly worker (limited positions available). Please contact me for details.

If you are from outside of WVU, please consider applying to our NSF REU program on human-swarm interaction.

Exchange/Visiting Positions

Please contact me if you are interested in visiting IRL. Generally, you would need to prepare your own funding support for these activities.

Why Future Warehouses May Look Like Monkey Houses

by Yu Gu, posted in Future, Ideas, Robotics, Uncategorized

The current-generation Amazon robot assisted warehouses are painfully boring to watch, once you realized how time, energy, and space inefficient they are. Each piece of merchandise has to travel on racks maybe thousands times heavier than itself, at a slow speed, through heavy traffic, while the majority of the warehouse volume (> 80%) is left unused.

Vision: I think most goods can be simply tossed up in the air by robots and be caught by other robots at distances. The future warehouses, which I would like to call them Monkey Houses, should be highly dynamic and densely filled with flying objects. It will improve the throughput of a same size warehouse by more than an order of magnitude and drastically reduce the energy consumption compared to the current systems.

Justification: while humans occasionally use throwing and catching for object handoff (e.g., sports), it is not generally considered as a reliable method, especially when there are multiple objects flying simultaneously. Robots, on the other hand (no pun intended), can be particularly good at this. This includes estimating object motion, performing fast and precise control actions for object catching, simultaneously tracking the trajectory of multiple flying objects, as well as communicating and coordinating with thousands other robots in making plans. With these super-human abilities (i.e., speed, precision, reliability, memory, and communication) of future robots, the engineering trades of future systems design often shift toward counter-(human)-intuitive directions.

More Detailed Vision: each rack in the warehouse will be a stationary robot that can throw and catch objects. Each type of object will have a g-loading rating, dictating how far it can fly in one hop (the packaging of some future goods may have to be redesigned to be better suitable for flying). The goods may go through multiple hops (i.e., catch and throw by robots in between) before reaching final destinations. All object information is shared and an air traffic management system will ensure objects flying pass each other with safe clearances. Like goods, small robots can also be tossed up in the air. They can intersect other flying objects to improve the flexibility of stationary rack robots…

Thinking beyond the warehouse settings, it is conceivable that the main mode of object handoff for robots in the future would be throwing and catching, once the reliability of such systems exceeds human’s capabilities. Compared to the continuous-contact object handoff between two robots, throwing and catching involves much less complex robot-robot interactions and thus is far simpler and robust for robots to perform. This would have many implications to the design of other future systems. For example drone delivery can be performed by throwing packages to balconies equipped with catching robots (or just baskets with nets). Battery changes for drones could be done by simply tossing batteries up and down. Exchange of cargos (and passengers) between two self-driving vehicles on the highway could be accomplished through the air. What other cool applications of robot throwing and catching can you think of?