Manager of Applied Science @ Amazon Fulfillment Technologies & Robotics. Berliner. Opinions are my own.

Berlin, Deutschland
Joined September 2013
Sebastian Höfer retweeted
Amazon's Sebastian Hoefer (@hoeferse) discusses his team's work on visual defect detection at @CVPR — including the Kaputt dataset and #CVPR2026 challenge. Learn more about the novel dataset: amzn.to/4oe4nHy
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Sebastian Höfer retweeted
Attending #CVPR2026 in Denver? We'll be at booth 349 showcasing our latest research with live demos throughout the conference. Stop by to meet our scientists and see their work in action. Check out the full booth schedule:
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Sebastian Höfer retweeted
🚀 Get ready to build anomaly detection models that actually work in production! And win a share of $25,500 USD total prize pool! VAND 4.0 @CVPR 2026. Participate in the Kaputt2 Challenge! sites.google.com/view/vand4-…
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Sebastian Höfer retweeted
Introducing NotebookLM for arXiv papers 🚀 Transform dense AI research into an engaging conversation With context across thousands of related papers, it captures motivations, draws connections to SOTA, and explains key insights like a professor who's read the entire field
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Sebastian Höfer retweeted
Mobile ALOHA's hardware is very capable. We brought it home yesterday and tried more tasks! It can: - do laundry👔👖 - self-charge⚡️ - use a vacuum - water plants🌳 - load and unload a dishwasher - use a coffee machine☕️ - obtain drinks from the fridge and open a beer🍺 - open doors🚪 - play with pets🐱 - throw away trash - turn on/off a lamp💡 Project website: mobile-aloha.github.io/ Co-lead @tonyzzhao, advised by @chelseabfinn (amazing photographing from @qingqing_zhao_ )
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Sebastian Höfer retweeted
Introducing Robo360 dataset 🚀, the first real-world omnispective multi-view and multi-material robotic manipulation dataset. Robo360 captures synchronized multi-modal robot-object interaction data (video, audio, proprioception, control) to facilitate research in dynamic novel-view synthesis, robot learning, physics understanding, world models, and more! Code and data coming soon!
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Sebastian Höfer retweeted
Huge thanks and congrats to my coauthors @lvminzhang @magrawala #ControlNet for winning the Marr Prize /best paper award @ICCVConference #ICCV2023 Join us this afternoon at Oral 1:30 PM “Paris Sud” and Poster 2:30~4:30 PM “Foyer Sud 156”
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Sebastian Höfer retweeted
DINOv2, the cutting-edge computer vision model trained through self-supervised learning to produce universal features, is now available under the Apache 2.0 license. Onward with open source AI.
Today we’re announcing two new updates in our computer vision work — a new, expanded license for our DINOv2 model and the release of FACET, a comprehensive new benchmark dataset to help evaluate and improve fairness in vision models. More details ➡️ bit.ly/3L35E1U 🧵
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Sebastian Höfer retweeted
AI drone racer finally beats human world champions! Recipe: • state estimation with KF + gate-based measurements as additional KF updates • small deep RL policy trained in sim • residual controllers on top Nature article: nature.com/articles/s41586-0…
We are thrilled to share our groundbreaking paper published today in @Nature: "Champion-Level Drone Racing using Deep Reinforcement Learning." We introduce "Swift," the first autonomous vision-based drone that beat human world champions in several fair head-to-head races! PDF (open access): nature.com/articles/s41586-0…. Video: youtu.be/fBiataDpGIo The "Swift" AI drone combines deep #reinforcementlearning in simulation with data collected in the physical world. This marks the first time that an autonomous mobile robot has beaten human champions in a real physical sport designed for and by humans. The key difference to previous achievements, like IBM’s Deep Blue winning against Gary Kasparov at chess in 1996 or Google DeepMind’s AlphaGo crushing the top champion Lee Sedol at Go in 2016, is that the previous feats were limited to board games or video games. This work represents a milestone for mobile robotics, machine intelligence, and beyond, which may inspire the deployment of hybrid learning-based solutions in other physical systems, such as autonomous vehicles, aircraft, and personal robots, across a broad range of applications. Kudos to my coauthors @kaufmann_elia @l_bauersfeld @antoniloq, the pilots @captainvanover @bmsthomas, and our supporters @m_schaepman @UZH_en @UZH_Science @UZHspacehub @ERC_Research @nccrrobotics
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Incredibly, the placebo effect is (mostly) not real. It is a result of statistical confusion. Whenever you have a group with extreme values, they tend to exhibit regression to the mean. Eg. on average, sick people tend to become more healthy over time. Thus if you give one group medicine, and one group placebo, the placebo group will also tend to get better over time, because of regression to the mean. People have then misinterpreted this to think that it is the placebo pill that actively does this. If you want to demonstrate a placebo effect, you have to construct a study where there are three groups: • A. treatment • B. placebo • C. no treatment, no placebo If B and C get different outcomes, that would demonstrate a placebo effect. When this has been tried, mainly there has been no provable placebo effect. See the paper in the screenshot. (There is some evidence for an effect for pain, but this get's into a slightly different debate.) The fact that the placebo effect is mainly not real, fortunately frees us from having to come up with convoluted explanations, as to why the placebo effect would work even when we tell the patient that it is a placebo, as in the quoted tweet.
what are your wildest ideas as to why the placebo effect has an effect even when you explicitly tell them it's a placebo
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In case it might be useful: AI teaching material on BSc level: user.tu-berlin.de/mtoussai/t… latex sources: github.com/MarcToussaint/AI-…
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We’re proud to announce that DeepMind and the Brain team from @Google Research will become a new unit: 𝗚𝗼𝗼𝗴𝗹𝗲 𝗗𝗲𝗲𝗽𝗠𝗶𝗻𝗱. Together, we'll accelerate progress towards a world where AI can help solve the biggest challenges facing humanity. → dpmd.ai/google-deepmind
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Sebastian Höfer retweeted
I love how absurd this is
can a neural network learn to walk as a physical object in a physics simulation? here I train walking neural nets with an evolutionary algorithm. The input nodes/feet are activated by sine waves at learned phases & connections between two neurons extend based on their difference
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Sebastian Höfer retweeted
Twitter’s Recommendation Source Code Twitter has open-sourced much of their recommendation source code, a first for a large social media company! Would love to see more of this blogpost: blog.twitter.com/en_us/topic… github: github.com/twitter/the-algor… ml github: github.com/twitter/the-algor…
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Sebastian Höfer retweeted
I didn't sign "the letter". Current AI poses lots of risks, but describing these systems as "ever more powerful digital minds" that no one can control is likely to make the problem even worse. What's needed: more transparency and better public discourse.
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Sebastian Höfer retweeted
Introducing ALOHA 🏖: 𝐀 𝐋ow-cost 𝐎pen-source 𝐇𝐀rdware System for Bimanual Teleoperation After 8 months iterating @stanford and 2 months working with beta users, we are finally ready to release it! Here is what ALOHA is capable of:
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Sebastian Höfer retweeted
The FIRST commercialized humanoid robot!! Congrats @agilityrobotics @JonathanWHurst
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Sebastian Höfer retweeted
Wie wir gemeinsam mit @AmazonNewsDE die IT-Talente von morgen empowern? Durch unser Inhouse-Mentoring-Programm 🚀 Nicht nur Schüler*innen profitieren von Mentoring, sondern auch Student*innen und Berufseinsteiger*innen. 👉youtube.com/watch?v=H-oBywR-…
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Very sad news. Everyday Robots developed general purpose robots that could clean cafeteria tables, separate rubbish from recycling & could even open doors. The robots use AI & reinforcement learning to adapt to new situations & improve their skills. #ai wired.com/story/alphabet-lay…
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