Has anyone encountered the Stretch robot from Boston Dynamics in any warehouses? If so - how smoothly does it actually run in terms of package handling and errors? I’ve seen plenty of demos online, yet all the boxes are presented in a way that just isn’t realistic? (They’re perfectly stacked and flat) Just curious as I’ve been looking for an answer but nothing online. Is the technology as reliable as they’re stating? submitted by /u/roboticist-666 [link] [Kommentare]

It's obviously not the publicly traded company, but what exactly is this? Just a token that's intended to cause confusion? I was looking for the real company's stock on Coinbase and this was the only thing came up. submitted by /u/jpnoa [link] [Kommentare]

If the only global currency is BitCoin and all other currencies no longer exist .... How much will a two story house cost in BTC? You are not escaping the fiat system. You are just rebuilding it with a digital kill switch to your wealth that these same owners will use against you. Have a nice day. :) submitted by /u/InsightKnite [link] [Kommentare]

Do you guys get a miccai grants result? I do not receive any mail. Don’t I accept? submitted by /u/CrazyIndependent7436 [link] [Kommentare]

Escaping the fiat matrix requires mastering your own mind, ignoring the noise, and unlearning decades of economic conditioning. submitted by /u/sylsau [link] [Kommentare]

Hello everyone! I’d like to introduce Earnboard, a new platform where Web3 and crypto projects can launch rewarding campaigns that activate and reward their communities. Projects can create campaigns with a prize pool, define social and community tasks, review user submissions, track campaign points and leaderboards, and distribute rewards through five models, including raffles and proportional distributions. Projects can review submissions themselves or use Earnboard’s Managed Review service. The full campaign prize pool goes to the community. For users, Earnboard offers a place to discover projects, complete campaigns tasks, submit their posts, earn campaign points, build their Earnboard Points ranking, and receive rewards for the engagements they create for projects. We also provide Creator Guides and smaller Earnboard Tasks so users can learn about the platform and participate while waiting for project campaigns. Earnboard is still new and small, and we want to be transparent: we do not have active partner campaigns yet. We are currently looking for our first projects interested in launching rewarding campaigns. Meanwhile, users are welcome to register, explore the platform, complete Earnboard Tasks, and become familiar with how everything works. Projects can also use our automated demo environment to create a test campaign and experience the complete flow in approximately seven minutes, without real funds or real users. Earnboard also has a mascot called PEEP. Since PEEP became part of the platform’s identity, we decided to give him a contract address and turn him into a memecoin too. However, PEEP is an additional community element, not Earnboard’s primary product or objective. The platform’s main purpose remains providing reliable campaign and reward infrastructure for projects and their communities. We invite users, creators, community members, and Web3 projects to explore Earnboard and grow with us from the beginning. I don't want to break the rules here, so not posting links, but you can find us under the 'earnboard' username on X! Thanks! Thank you for giving us the space to introduce Earnboard. submitted by /u/cryptohiddengems [link] [Kommentare]

Follow-up to my v2 trajectory post. The RViz trajectory had visible zig-zag jitter even when the robot was stationary. Before deciding what filter to apply, I wanted to actually measure the noise and understand what's driving it. The problem The v2 system uses a physically fixed AprilTag (tag1) as a world frame anchor. The Pi detects it each frame, inverts the camera→tag transform to get world→camera, and publishes that as a TF. The zig-zags in the trajectory come from frame-to-frame instability in that pose estimate. The root cause is AprilTag PnP pose ambiguity — the solver has two valid geometric solutions for a planar tag and flips between them. The flip shows up as a large swing on one axis, typically ±15cm, even with the camera stationary. On top of that, small angular errors get amplified into position noise through the matrix inversion: at ~74cm tag distance, a 5° rotation error becomes ~6.5cm of position noise in world frame. The question I wanted to answer before touching the filter: how much does tag size actually move the needle? Method Added a single_tag_world_mode flag to config so ManualTracker can run with just the world anchor tag in frame — no chase target needed. Camera held stationary, pointed directly at the tag, for ~2–3 minutes per condition. Raw camera-frame poses recorded automatically to JSON. Four conditions: 5cm and 20cm printed tags, each with room lights on and off. All four plots below share identical axis scales so the distributions are directly comparable. Results Condition σ X σ Y σ Z (depth) 5cm — lights off 3.4 cm 0.5 cm 4.5 cm 5cm — lights on 5.1 cm 1.7 cm 3.7 cm 20cm — lights on 2.7 cm 0.4 cm 1.4 cm 20cm — lights off 2.1 cm 1.0 cm 1.7 cm (Images: 5cm lights off → 5cm lights on → 20cm lights on → 20cm lights off) What the plots show Tag size dominates. Going from 5cm to 20cm cuts depth noise by roughly 3x. The distributions tighten and become more unimodal — the PnP flip signature (broad or bimodal histogram on X and Z) is clearly visible in the 5cm sessions and largely absent in the 20cm sessions. Lighting is secondary. For the 5cm tag, lights-on is actually worse on X (σ 5.1 vs 3.4cm), likely because uncontrolled ambient light causes glare that degrades corner localization on a small tag. For the 20cm tag the lighting effect is small enough that it's not the thing to optimize. Best condition across all three axes simultaneously: 20cm + lights on (σX=2.7cm, σY=0.4cm, σZ=1.4cm). What's next This experiment was groundwork, not a fix. The noise is reduced but still present — 2cm+ std dev on X and Z with a stationary camera is not acceptable for a usable world frame. The next step is a filter, but the right choice (EWMA, velocity gate, Kalman, or some combination) depends on understanding the noise characteristics, which is what this data was for. Still deciding. Open to suggestions from anyone who's dealt with PnP jitter on planar markers before. References Post history v2 trajectory post v1 tag chaser PiCar-X introduction Hardware / code PiCar-X on Amazon Git repo submitted by /u/okineedaplan [link] [Kommentare]

not sure if this is the right flair 😕 I had put this post up on r/gradadmissions but i feel like I'd get a better demographic that knows the field better here submitted by /u/Proud_Imagination_94 [link] [Kommentare]