Gradient-based planning, mapping and execution

this post continues from the previous post <Gradient-based trajecotry planning>, because i became even busier. in fact, i should work on my presentation slide for my talk at the University of Washington tomorrow (sorry, Yejin and Noah!), and probably because of that, i decided to push it a bit further. the main assumption i made in the previous slide was that our bot has access to the entire map. this is a huge assumption that does not often hold in practice. instead, i decided to restrict the visibility of our bot. it will be able to see the obstacles in

Gradient-based trajectory planning

this semester has been completely crazy for me, and i anticipate that this madness will only worsen over the next couple of months. of course, because of this crazy schedule, my brain started to revolt by growing a doubt inside me on how much i trust gradient descent. crazy, right? yes. i then succumbed to this temptation and looked for some simple example to test my trust in gradient descent. yes, i know that i should never doubt our lord Gradient Descent, but my belief is simply too weak. so, i decided to use gradient descent for simple trajectory planning

A short thought on watermarking

so, it looks like watermarking is a thing that is coming back to its (controversial) life. the idea of watermarking is to enable content producers to mark their own contents so as to track where those contents are being consumed without introducing too much of disruption. one of the simplest watermarking techniques i run into quite often is on a plan with their entertainment system; when you watch a movie on an airplane, you often notice the airline code (e.g. “DL” in the case of Delta) embroiled on the screen once a while. i presume the heightened interest in watermarking

Expectile regression

i often find myself extremely embarrassed by myself, because i learn of concepts in machine learning that i should’ve known as a professor in machine learning but had never even heard of before. one latest example was expectile regression; i ran into this concept while studying Kostrikov et al. (2021) on implicit Q learning for offline reinforcement learning together with Daekyu who is visiting me from Samsung. in their paper, Kostrikov et al. present the following loss function to estimate the $\tau$-th expectile of a random variable $X$: $$\arg\min_{m_{\tau}} \mathbb{E}_{x \sim X}\left[ L_2^\tau (x – m_{\tau}) \right],$$ where $L_2^\tau(u) =

Defining emergence

so, apparently, emergence has become a hot topic on twitter while i was away in Kigali attending ICLR, moto-taxing in Kigali, injuring myself and breaking my phone running and tracking, seeing a majestic group of gorillas and being back at AIMS Rwanda after 4 years. the mountain gorillas were majestic. i do not want to discuss any particular paper/tweet/blog, because this topic seems to attract a weird set of people arguing for weird things, when in fact there are just a couple of different views into a single phenomenon, which is only natural in science and engineering. that said, if

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