Matchmaking Problems and Solutions

🚨 New Hacking Academia video out on Matchmaking Problems and Solutions in researchIt's *that* time of year again - submissions for the main international robotics conference ICRA2027 close in just over a month.In today's video I cover:💕 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝 𝐚 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐭𝐨 𝐬𝐨𝐥𝐯𝐞. If the task you're developing solutions for is already near perfectly solved, it doesn't give you room for improvement (and improvement is usually hard to come by). You can typically find that room for improvement by considering the practical, real-world grounding for the task you're performing:🔹 how can your robot perform that task in more challenging and varied environmental conditions?🔹 how can your robot perform equally well with less training, or with less access to relevant training data🔹 how can your robot become better at self-assessing when it is performing well or not🔹 how can your robot achieve a level of accuracy, coverage, generality or robustness to adversarial interference that current methods cannot?And so on: there is a reason robots are still not widely deployed, and you can find research inspiration in the gaps that remain to be bridged...💕 𝐲𝐨𝐮𝐫 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐧𝐞𝐞𝐝𝐬 𝐭𝐨 𝐏𝐀𝐑𝐓𝐈𝐂𝐔𝐋𝐀𝐑𝐋𝐘 𝐟𝐢𝐭 𝐭𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. There are many "generic" ways to improve performance: more training, more compute, using more data over time, using more sensors. Much of the key work has been done here already: there is a higher bar for demonstrating why your contribution is particularly noteworthy against all that prior work. A better situation is one where some key aspect of how your proposed solution *particularly* fits/solves the type of problem.For example, in the early days of GANs - Generative Adversarial Networks - some networks demonstrated an impressive ability to re-generate real world photos under different conditions - for example turning a summer scene into a winter one - an application with great potential utility in making "all season" localization systems.Another example is a localization system that uses multiple sensors where one sensor works particularly well in large open bland areas (e.g. GNSS in the middle of a desert) and one works well in cluttered environments with lots of landmarks (e.g. a camera in a city streets).These are situations where the contribution moves beyond just "more/better is better" and has *specific* reasons why the change is particularly helpful.This doesn't mean general improvements are not helpful, but the more intuition you have that your proposed approach is particularly suited to the problem, the more likely you will end up at a valuable, and publishable, contribution.YouTube: https://youtu.be/VklLyfTGk5M#research #academic #ICRA2027 #robotics #computervision #artificialintelligence #publishing #papers #writing #advice