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Introducing the Robora 3D Builder by our Co-Founder, Guang Cheng: a web based, drag and drop interface for rapid robotics prototyping. Designed for hobbyists and DIY enthusiasts, the MVP includes 5 - 8 essential hardware modules: wheeled bases, motors, servos, LiDAR, sensors, and an integrated starter brick for power... show more

Robora

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40,647 views • 11 months ago •via X (Twitter)

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Techniahqrobot | humanoid robots

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75,858 views • 1 month ago

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Lukas Ziegler

20,361 views • 2 months ago

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RAF

15,720 views • 2 years ago

Build a humanoid robot yourself! 🪚 OpenArm is an open-source humanoid robot. It comes with full CAD, control code, firmware, and simulation tools, everything needed to build, modify, and operate it. The arms are designed to be compliant and backdrivable. Teleoperation is supported, with force feedback and real-time gravity compensation so operators can guide the arm naturally. Super important, on the simulation side, OpenArm works with platforms like MuJoCo and Isaac Sim, letting developers test policies in virtual environments before running on hardware. Assemble it yourself from a kit or get it prebuilt, the goal is accessibility for research labs, small teams, and enthusiasts. The project is run by Enactic in Tokyo, Japan, and aims to lower the barrier for experimenting with dexterous manipulation. Let's put robotics in mainstream! 🔥🔥 P.S. I put the project page in the comments!

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Lukas Ziegler

233,110 views • 9 months ago

It's a 3D printer, and 3D assembly station! 🖨️ The Functgraph developed at Meiji University starts as a regular 3D printer but upgrades itself into a mini factory. It can print parts for its own tools, pick them up, clean them, and put them together, all by itself. Think of it like a robot that can 3D print a spatula, assemble it, and then use it to flip pancakes. 🥞 Instead of just printing objects, the Functgraph can actually perform physical tasks, like folding laundry or slicing vegetables, by using printed and assembled tools. It’s a step toward the idea of a robot that can download "apps" as physical skills, much like your phone downloads software! 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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Lukas Ziegler

139,982 views • 3 months ago

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55,224 views • 23 hours ago

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Bezi

15,295 views • 1 year ago

Introducing the composable reputation API for agents. The FairScale Agent API is now open to any team building on Solana Gate access, transactions and assess credibility in real time. Score any agent or wallet based on behaviour, deployer, counterparties, and network. Use our FairScore, or define your own by weighting features through a custom scoring endpoint.

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FairScale

16,896 views • 4 months ago

Robora x Lemorele We are excited to announce the integration of the Robora P300 (R) model. An exclusive model developed in partnership with Lemorele, features a custom Robora-exclusive SKU and hardware configuration designed to support high-performance, wireless video streaming for AI training and perception. We will integrate this specialized unit into our VLA Module. The Robora P300 (R) enables seamless, real-time capture of high-definition HDMI video from external cameras, allowing users to wirelessly feed visual data directly into the VLA training pipeline. This enhances capabilities such as multi-view learning, remote annotation, and real-world AI fine-tuning, all without requiring tethered camera setups. While the full Robora robotics platform is still in development, users will be able to use the Robora P300 (R) hardware with their existing iOS or Android devices, enabling them to begin collecting and streaming visual data for VLA from mobile platforms. As part of our commitment to the Robora community, we will be offering exclusive giveaways of the Robora P300 (R) hardware to token holders, giving early supporters the opportunity to contribute directly to Robora’s growing AI training ecosystem and to gain early access to the tools that power the future of robotics and intelligent vision.

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Robora

19,276 views • 10 months ago

Clerk Billing update! 🎉 The team has been hard at work building the foundations of the most compelling user billing product for SaaS companies. No need to integrate with any payment provider, or watch for web hooks, or write payment code. It all just works out of the box. And comes with User Entitlements BUILT-IN! Check it out 🎥 If you're interested in early access, DM me and I'll toss you on the early access list 🤝 Also, if you're interested in building with me, I'm hiring for senior product, senior design and senior engineering roles! DM me with a link to your LinkedIn/portfolio/github 👍

Clerk Billing update! 🎉 The team has been hard at work building the foundations of the most compelling user billing product for SaaS companies. No need to integrate with any payment provider, or watch for web hooks, or write payment code. It all just works out of the box. And comes with User Entitlements BUILT-IN! Check it out 🎥 If you're interested in early access, DM me and I'll toss you on the early access list 🤝 Also, if you're interested in building with me, I'm hiring for senior product, senior design and senior engineering roles! DM me with a link to your LinkedIn/portfolio/github 👍

Drew Wilson

53,985 views • 1 year ago

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Alex Barashkov

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Runway

80,385 views • 8 months ago

Blended-NeRF: Zero-Shot Object Generation and Blending in Existing Neural Radiance Fields paper page: Editing a local region or a specific object in a 3D scene represented by a NeRF is challenging, mainly due to the implicit nature of the scene representation. Consistently blending a new realistic object into the scene adds an additional level of difficulty. We present Blended-NeRF, a robust and flexible framework for editing a specific region of interest in an existing NeRF scene, based on text prompts or image patches, along with a 3D ROI box. Our method leverages a pretrained language-image model to steer the synthesis towards a user-provided text prompt or image patch, along with a 3D MLP model initialized on an existing NeRF scene to generate the object and blend it into a specified region in the original scene. We allow local editing by localizing a 3D ROI box in the input scene, and seamlessly blend the content synthesized inside the ROI with the existing scene using a novel volumetric blending technique. To obtain natural looking and view-consistent results, we leverage existing and new geometric priors and 3D augmentations for improving the visual fidelity of the final result. We test our framework both qualitatively and quantitatively on a variety of real 3D scenes and text prompts, demonstrating realistic multi-view consistent results with much flexibility and diversity compared to the baselines. Finally, we show the applicability of our framework for several 3D editing applications, including adding new objects to a scene, removing/replacing/altering existing objects, and texture conversion.

Blended-NeRF: Zero-Shot Object Generation and Blending in Existing Neural Radiance Fields paper page: Editing a local region or a specific object in a 3D scene represented by a NeRF is challenging, mainly due to the implicit nature of the scene representation. Consistently blending a new realistic object into the scene adds an additional level of difficulty. We present Blended-NeRF, a robust and flexible framework for editing a specific region of interest in an existing NeRF scene, based on text prompts or image patches, along with a 3D ROI box. Our method leverages a pretrained language-image model to steer the synthesis towards a user-provided text prompt or image patch, along with a 3D MLP model initialized on an existing NeRF scene to generate the object and blend it into a specified region in the original scene. We allow local editing by localizing a 3D ROI box in the input scene, and seamlessly blend the content synthesized inside the ROI with the existing scene using a novel volumetric blending technique. To obtain natural looking and view-consistent results, we leverage existing and new geometric priors and 3D augmentations for improving the visual fidelity of the final result. We test our framework both qualitatively and quantitatively on a variety of real 3D scenes and text prompts, demonstrating realistic multi-view consistent results with much flexibility and diversity compared to the baselines. Finally, we show the applicability of our framework for several 3D editing applications, including adding new objects to a scene, removing/replacing/altering existing objects, and texture conversion.

AK

62,768 views • 3 years ago