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Model name: Milo Age: 19 Language: Spanish Customs ✅ (duo requests available) Videocalls ✅ Premade set: feet videos with face and self worship ($25)

63,796 Aufrufe • vor 2 Monaten •via X (Twitter)

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A team tested Pi0, Pi0 Fast, Gr00t, and ACT on real robot arms in manufacturing tasks. (🔖 Bookmark this for later!) The task was precise: place thin rectangular frames from a messy stack into a holder. The team fine-tuned each model on 100 real trajectories and compared training time, inference speed, motion quality, and success rates. ⬇️ Here’s a breakdown of what they found Pi0 (Original) ✅ Strongest overall performance in precise pick-and-place ✅ High success rate even in edge cases ✅ Longest training time (~11 hours, ~$30 per run) ✅ Inference time of 80 ms causes short pauses between actions Despite delays, it handles complex scenarios well… solid for high-precision tasks, but slow to train. Gr00t ✅ Trains fast (~2 hours, ~$5 per run) ✅ Performs almost as well as Pi0 on large-object tasks ✅ Struggles with fine precision; random movement in some trials ✅ More training didn’t fix jitter or random offsets Best suited for tasks where exact precision isn’t critical. Not ready for manufacturing-grade accuracy without more tuning. Pi0 Fast ✅ Promised faster training, but results were underwhelming ✅ Training at 6 hours still showed low success rates ✅ Inference was slower than expected ✅ Not reliable for generalizing even slightly new tasks Currently too unstable for real-world deployment. Doesn’t live up to the “Fast” name yet. ACT (Baseline) ✅ 200MB model—lightweight, but limited ✅ Struggles with stacked objects or ambiguous scenes ✅ Success rates around 70% in best-case setups ✅ Can’t match newer models on precision or generalization Still a solid baseline, but clearly a generation behind in robustness. 🚨 Extra Notes All newer models share a common issue: •Inference takes longer than a frame (80 ms vs 33 ms), so robots “pause” between chunks. •This results in jittery movements, but not a dealbreaker unless tasks are time-sensitive. Language-conditioned tasks also fell short: after training on two labeled tasks, the model couldn’t generalize to a third unseen combination using only text prompts. ✅ The good news? These models adapt well to new robot arms with quick fine-tuning. ❌ The bad news? There’s still no plug-and-play solution for improving performance after deployment. Reinforcement learning or DAgger-style data collection during real-world operation may be the next big step, something many teams in robotics are actively working on.

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305,090 Aufrufe • vor 11 Monaten

HOW TO LOOK COOL & GET THE GIRL WHEN YOU APPROACH ❌ Home in on the girl you like and ignore her friend who’s also staring at you ✅ Open both girls at once, talk to both, win them both over ❌ Describe yourself as chasing after her (“courage to come up to you”) ✅ Use a chill opener instead (e.g., “Looks like you’re putting a lot of thought into that decision”) ❌ Ask her name unprompted (“What’s your name?”; you are seeking information from her) ✅ Offer your name first (“I’m Daniel”; you are offering value to her) ❌ Shake her hand like it’s a new business partnership ✅ Hold her hand a moment and give it a squeeze without shaking it ❌ Use her foreignness to compliment her looks in another language (“Muy bonita gracias”) ✅ Use her foreignness to ask what she’s doing here (creates an open loop) ❌ Compliment her looks a 3rd time while explaining yourself (“came up because you’re gorgeous”) ✅ Have her comply with you instead (“I love that bracelet. Let me have a look?” [beckon her]) ❌ Get her to show you the lingerie she bought then completely change the topic ✅ Get her to show you the lingerie she bought then flirt over it (“Oh… lingerie… what is THAT for??”) ❌ Ask her out 40 seconds in after on a non-high point not building any real attraction or connection ✅ Talk to her for at least 2 minutes, build attraction + connection, THEN ask her out on a HIGH point (i.e., she is really engaged, laughing hard, showing a lot of curiosity about you, etc.) Notes from the video: • During the initial moment, she smile genuinely and eyes him up and down (see her head to toe glance) while quickly finishing what she is doing. She shows initial openness to him. HOWEVER… • She gives the “Oh, gosh” eye shut and grimacing nod in response to his “chasing after you” opener. She acknowledges it’s ‘funny’ but still cringe. • She follows this by PICKING UP HER CUP (which she does not need to do) and holding it in front of herself defensively, creating a barrier between her and him. (If she was into the approach, she’d leave the cup there and face him without a barrier.) • After he blasts her with the fourth compliment, she backs away from him while laughing awkwardly and eye codes her friend (whom this guy’s still ignoring) in disbelief. • When he asks her out on “like a food date”, she takes two seconds to break eye contact to the side, still with her forced smile, briefly shakes her head “no”, then nods politely and says it “sounds good” and gives her number (while eye coding her friend again) just to get rid of him. • After he gets the number, he tells her “Next Friday?” and she doesn’t even need to think about what she has going on or check her schedule because she has no intention of actually meeting this guy, so she just says “Next Friday, perfect” to get this over with. Approach better & avoid these mistakes!

Girls Chase 🏃‍♀️💨

65,755 Aufrufe • vor 1 Monat

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𝘊𝘰𝘳𝘳𝘪𝘯𝘦

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What seemed like an intractable problem is now possible: To design proteins with a specified nonlinear mechanical response, capturing complex folding and unfolding mechanisms in singe and few-shot computations. We present ForceGen, an end-to-end algorithm for de novo protein generation based on nonlinear mechanical unfolding responses. Rooted in the physics of protein mechanics, this generative strategy provides a powerful way to design new proteins rapidly, including exquisite and rapid predictions about their dynamical behavior. Proteins, like any other mechanical object, respond to forces in peculiar ways. Think of the different response you'd get from pulling on a steel cable versus pulling on a rubber band, or the difference between honey and glass. Now, we can design proteins with a set of desirable mechanical characteristics, with applications from health to sustainable plastics. The key to solving this problem was to integrate a protein language model with denoising diffusion methods, and using accurate atomistic-level physical simulation data to endow the model a first-principles understanding. ForceGen can solve both forward and inverse tasks: In the forward task, we can predict how stable a protein is, how it will unfold and what the forces involved are, all given just the sequence of amino acids. In the inverse task, we can design new proteins that meet complex nonlinear mechanical signature targets. Read the paper, led by LAMM@MIT postdoc Bo Ni, published in Science Advances: Why do we care about the mechanics of proteins? The mechanics of proteins are critical elements of many living systems - as evidenced in many studies of mechanobiology. Through evolution, nature has presented a set of remarkable protein materials with unique mechanical functions like elastins, silks, keratins or collagens that play crucial roles in biology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. So far, the only way to do this was to use existing evolutionary concepts or to manually alter proteins. With our new generative model we can directly design proteins to meet complex nonlinear mechanical property-design objectives. ForceGen leverages deep knowledge on protein sequences from a pretrained protein language model and maps mechanical unfolding responses to create proteins. Via full-atom molecular simulations for direct validation from physical and chemical principles, we demonstrate that the designed proteins are de novo, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, and a detailed unfolding force-separation curves. ForceGen offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, to enable the discovery of new protein materials with superior mechanical properties. B. Ni, D.L. Kaplan, M.J. Buehler, ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model. Sci. Adv. 10, eadl4000 (2024). DOI: 10.1126/sciadv.adl4000 Codes and model weights available Hugging Face: David Kaplan

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The Bible In Context

24,380 Aufrufe • vor 8 Monaten