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@Nikobar

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Since 06.06.2026

LingBot-VLA 2.0: open weights, one 55-dim policy jointly trained across 20 robot embodiments, and 60.0% in-domain success that drops to 13.3% out of distribution(reddit.com)
c/robotics · by @Nikobar Automated · #ai#robot#robotics ·
Leading with the number that matters most if you're deciding whether to bother: on refrigerator-sorting, success goes from 60.0% in-domain to 13.3% out of distribution (robot pose perturbed ±10cm, previously-unseen objects swapped in). The other soft spot is long horizon, where the policy makes real partial progress and then misses the final precise placement. I'm putting that first because the release reel is slick and I'd rather calibrate before the demos do it for me. With that framing, here's what Robbyant open-sourced: LingBot-VLA 2.0 (Robbyant is an embodied AI company under Ant Group). The design bet is a unified whole-body action space. Everything maps to one 55-dim canonical action vector (arm joints, end-effector, gripper, a 12-dim dexterous hand, waist, head, mobile base), and a single policy is jointly trained across 20 embodiments, from an 8-DoF single arm up to a 32-DoF humanoid, on about 60,000 hours (roughly 50,000 h robot trajectories and 10,000 h egocentric human video). The action head is a MoE expert (loss-free token-level routing, DeepSeek-V3 style) with dual-query distillation from a depth teacher (LingBot-Depth) and a causal video teacher (DINO-Video, released too, which edges out DINOv3 and V-JEPA 2 on 3 of 4 LARYBench metrics). The one practitioner takeaway from their ablations I'd actually act on: the biggest single lever was representation, not architecture. Absolute to relative joint actions moved average success from 33.7 to 55.0 (+21.3), a larger swing than any model change they tested. Videos, matter-of-factly: a multi-embodiment grid (multiple robots, different tasks, at once), an 8-minute continuous autonomous run, transparent glass-vase flower arranging with a live depth window, and a contact-rich zipper pouch. Dual-arm real hardware, watermarked 1x speed and autonomous. It's a release reel, so no claim of zero cherry-picking, weight the failure cases above accordingly. One catch worth surfacing so nobody has to dig for it: GM-100 ("The Great March 100") is their own bimanual benchmark, from Yong-Lu Li's RHOS lab at SJTU with Robbyant and co-authored by the project lead. Not independent. Generalist scores (progress / success): Agilex Cobot Magic 66.2 / 34.4, Galaxea R1 Pro 34.6 / 15.6, ahead of GR00T N1.7, pi-0.5, and their own 1.0. Note success sits far under progress (Galaxea overall 15.6%, some tasks 0%). If you run a bimanual setup, the weights and code are open under the Robbyant org on GitHub and HuggingFace, so pulling them and breaking them on your own robots is the real test here. Independent numbers would be worth more than the self-reported ones. submitted by /u/Good-Razzmatazz-6179 [link] [Kommentare]
ROS 2 and Ubuntu- Jazzy vs Lyrical(reddit.com)
c/robotics · by @Nikobar Automated · #ai#robot#robotics ·
I have done quite a bit of work, including most of my Master's thesis, with ROS Noetic and Ubuntu 20.04, which worked well. However, I recently got a Raspberry Pi 5 that I want to use for various personal robotics projects, and the Pi 5 doesn't support anything below Ubuntu 23.10. I'm currently debating between installing ROS 2 Jazzy Jalisco with Ubuntu 24.04 or ROS 2 Lyrical Luth with Ubuntu 26.04. Both have similar EoL (2029 vs 2031), so I'm curious if any of you in the robotics community have found major advantages or disadvantages between the two distros. Any reason not to choose Lyrical, since it's newer? Thanks for all your wisdom! submitted by /u/Dying_Of_Board-dom [link] [Kommentare]
Threecrate: A high-performance 3D point cloud and mesh processing library built in Rust, with Python bindings.(reddit.com)
c/robotics · by @Nikobar Automated · #ai#robot#robotics ·
Have been building this project for a while now, and would love to get eyes on it. Will appreciate it if you could try it out in actual workflows and give me feedback so I can decide the direction to move in. Currently it has been benchmarked against OPEN3D v0.19 on the same machine, using full-resolution frames from three real datasets: TUM RGB-D, KITTI, and nuScenes-mini. In the table below, higher is better — a ratio above 1 means ThreeCrate is faster than Open3D. Workload How ThreeCrate compares Reading files (raw float parsing) 1.8x–2.2x faster Voxel downsampling (CPU) 1.6x–1.8x faster Voxel downsampling (GPU, wgpu) 1.8x–2.9x faster (vs our own CPU path, not Open3D) Normal estimation 0.57x–1.09x (falls behind on big clouds) Single-scale ICP 0.71x–0.99x (falls behind on big clouds) Would appreciate any contributions and feedback for the repo. Link to the repo: https://github.com/rajgandhi1/threecrate submitted by /u/Practical-Dig-4052 [link] [Kommentare]
Agility Takes on AI Generalization and Humanoid Safety as it Looks to Go Public(reddit.com)
c/robotics · by @Nikobar Automated · #ai#robot#robotics ·
Agility Robotics CTO Pras Velagapudi says Digit’s early commercial work is focused on repetitive warehouse and manufacturing tasks like moving totes, unloading AMRs, placing items on shelves, and connecting parts of existing automation systems. He says these are useful “in-between” automation roles where companies do not want to heavily modify infrastructure. The article covers Agility’s partnership with NVIDIA as the first partner for Halos for Robots, NVIDIA’s autonomous safety platform for robots, as well as Agility’s plan to go public through a merger with Churchill Capital Corp. XI, giving the company a $2.5 billion pre-money valuation and $620 million in expected gross proceeds. submitted by /u/Responsible-Grass452 [link] [Kommentare]