🏫hololive wacha-wacha high school festival🏫 Introducing the event member list! 👀✨ 🔽Member List - Day 1 #YuzukiChoco# #ShiroganeNoel# #YukihamaLamy# #MomosuzuNene# #OmaruPolka# #IsakiRiona# #RindoChihaya# #KikiraraVivi# - Day 2 #Robocosan# #AkiRosenthal# #NatsuiroMatsuri# #TsunomakiWatame# #La#+Darknesss #HakuiKoyori# #IchijouRirika# #JuufuuteiRaden# Details will be announced at a later date! Stay tuned! 🙌 #ホロわちゃ学園祭#

Over the weekend we had the pleasure of hosting @unimalaya's Robocon team for a workshop at the lab. Always happy to facilitate events like this for students and researchers — just get in touch.

The dataset and paper based on the previous phase of community contributions are now live： Paper Link: Project Page: Dataset Link: Github Codebase: Using Pi0.5 + AXIS-100%, we achieved 88.8 overall success on LIBERO-Plus. For comparison: Vanilla Pi0.5 achieved 83.9 A RoboCasa-matched simulation baseline achieved 57.5 This clearly demonstrates that diverse, in-the-wild data provides substantial gains in model performance. And V2 is already in progress — significantly larger in scale, covering more embodiments and a wider range of atomic capabilities.

The first time a robot attempted "Wine Bottle in Bowl," its success rate was 15%. Then it crossed a threshold — and jumped to 95%. Not because the model was retrained. Because the code-based harness governing its execution had evolved itself. Today's VLA (Vision-Language-Action) models run open-loop. When a robot fails, there's no real-time correction — the feedback loop closes only after the episode ends, when a human reviews logs and manually adjusts parameters. That cycle is slow. It doesn't scale. Minor physical disturbances cascade into failures the system can't catch in the moment. Zetta ζ solves this across three timescales. During execution, high-frequency runtime critics monitor trajectory deviations and trigger recovery interventions before failures compound (action level). Failed rollouts are clustered by failure signatures, diagnosed through six causal layers, and the harness is minimally repaired (episode level). Only skills that generalize across held-out environments pass through the validation gate and are permanently committed to skill memory (iteration level). Base model weights stay frozen throughout. Only the code-based harness evolves. LIBERO-Pro: 34.5% → 90.8%. RoboCasa: 73.6% → 93.6%. Inference speed: 11.1× faster than the RPent baseline. Throughput: 1.7 → 35.1 episodes/min (20.6×). Learned skills transferred zero-shot across PnP-Sink, PnP-Cabinet, and PnP-Toaster tasks. Zetta ζ: An Efficient Closed-Loop Embodied Harness for Self-Evolving Physical Intelligence #EmbodiedAI# #Robotics#

🤖 Expensive robot data is scarce, so why not learn from our everyday first-person videos? This work turns human hand motion into robot actions to pretrain VLA models. 📰 Title: ACE-Ego-0: Unifying Egocentric Human and Robotic Data for VLA Pretraining 🔗 URL: 💡 Overview ACE-Ego-0 unifies robot demonstrations with egocentric human videos (Ego4D, EPIC-KITCHENS, and more) to pretrain Vision-Language-Action (VLA) models, trained on over 6,000 hours of combined data. 🔍 Challenges Solved Robot demonstrations are costly to collect, while human videos are cheap and abundant. But the two differ in action space, embodiment structure, temporal dynamics, and supervision quality, so naively mixing them breaks training. 🛠 Methodology & Proposed Approach ・Unifies actions in head-camera coordinates with 6D rotations, treating the human hand as an end-effector ・Encodes robot URDFs into morphology tokens via a GNN to absorb structural differences ・Chunks actions by consistent physical duration instead of fixed steps for temporal alignment ・Applies a reliability-weighted loss to noisy human videos, focusing on trustworthy position channels 📊 Use Cases / Results On RoboCasa it hits 72.8% average success (vs GR00T-N1.6 at 47.6%) and ~91% on RoboTwin 2.0. On a real bimanual robot it reaches 78.3% average (π0.5: 71.7%). Strikingly, on a task with only 34 robot demos, adding 419 human video episodes lifted success from 10% to 40%, a 4x gain. #RobotLearning# #VLA#