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What has been the hardest part of collecting training data for robotics projects?(reddit.com)
I'm researching how teams build datasets for robot learning and I'm curious what the biggest challenges are in practice. From what I've seen so far, collecting robotics data seems very different from standard computer vision datasets because you have to deal with sensor synchronization, demonstrations, real-world edge cases, and often much smaller datasets. One thing I'm still trying to understand is where most teams spend the majority of their time. For people working on robot learning, manipulation, navigation, or autonomous systems: Is data collection the main bottleneck? Is annotation and labeling the difficult part? Do you rely more on simulation or real-world data? What would you improve if you could rebuild your data pipeline from scratch? I'd love to hear some real-world experiences. submitted by /u/Vane1st [link] [Kommentare]
It's wild watching the market unwind like this(reddit.com)
This is absolutely insane and just continues to remind me of the COVID dip in March 2020. ETH is no longer #2, falling to #3 now to Tether. Many of the big alts are dropping BELOW their low ranges of the 2022/2023 bear market, lower than even where they were after the collapse of FTX. Crazy to see this all playing out. I am predominantly BTC, but also have had positions in ETH an ADA for a while now. I've been loading up aggressively on BTC this year in the 60k range, but the insane volatility on the alts has made me a bit more conservative with my moves on them. This will eventually find some sort of floor of support where it stabilizes, I don't know exactly where or when this will happen, so plan is to focus more on accumulating BTC for now. Crazy times. submitted by /u/ubermensch1001 [link] [Kommentare]