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)

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]