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Archive for February, 2018|Monthly archive page

Talk Picks: IROS 2017

In deep learning, Machine Learning, Robotics on February 10, 2018 at 1:06 pm

by Li Yang Ku (Gooly)

I was at IROS (International Conference on Intelligent Robots and Systems) in Vancouver recently (September 2017, this post took way too long to finish)  to present one of my work done almost two years ago. Interestingly, there are four deep learning related sessions this year and there are quite a few papers that I found interesting, however the talks at IROS were what I found the most inspiring. I am going to talk about three of them in the following.

a) “Toward Unifying Model-Based and Learning-Based Robotics”, plenary talk by Dieter Fox.  

In my previous post, I talked about how the machine learning field differs from the robotics field, where machine learning learns from data and robotics designs models that describe the environment. In this talk, Dieter tries to glue both worlds together. This 50 minutes talk is posted below. For those who don’t have 50 minutes, I describe the talk briefly in the following.

Dieter first described a list of work his lab did (robot localization, RGB-D matching, real time tracking, etc.) using model based approaches. Model based approaches matches models to data streams and controls the robot by finding actions that reaches the desired state. One of the benefits of such approach is that our own knowledge of how the physical world works can be injected into the model. Dieter then gave a brief introduction on deep learning and on one of his students work on learning visual descriptors in a self-supervised way, which I covered in a previous post. Based on the recent success in deep learning, Dieter suggested that there are ways to incorporate model based approaches into a deep learning framework and show an example on how we can add knowledge of rigid body motion into a network by forcing it to output segmentations and their poses. The overall conclusion is that 1) model based approaches are accurate within a local basin of attraction which the models match the environment, 2) deep learning provide larger basin of attraction in the trained regime, 3) Unifying both approaches give you more powerful systems.


b) “Robotics as the Path to Intelligence”, keynote talk by Oliver Brock

Oliver Brock gave an exciting interactive talk on understanding intelligence in one of the IROS keynote sessions. Unfortunately it is not recorded and the given slides cannot be distributed, so I posted the most similar talk he gave below instead. It is also a pretty good talk with some of the contents overlapped but under a different topic.

In the IROS talk, Oliver made a few points. First, he start out with the AlphaGo by Deepmind, stating that its success in the game go is very similar to the IBM Deep Blue that beats the chess champion in 1996. In both cases, despite the system’s superior game play performance, it needs a human to play for it. A lot of things that humans are good at are usually difficult to our current approach to artificial intelligence. How we define intelligence is crucial because it will shape our research direction and how we solve problems. Oliver then showed that defining intelligence is non-trivial and has to do with what we perceive by performing an interactive experiment with the audience. He then talked about his work on integrating cross model perception and action, the importance of manipulation towards intelligence, and soft hands that can solve hard manipulation problems.


c) “The Power of Procrastination”, special event talk by Jorge Cham

This is probably the most popular talk of all the IROS talks. The speaker Jorge Cham is the author of the popular PHD Comics (which I may have posted on my blog without permission) and has a PhD degree in robotics from Stanford university. The following is not the exact same talk he gave in IROS but very similar.