Scaling robotics datasets with video encoding

HuRo: Robotizing Human Videos for Scalable VLA Pretraining

The paper introduces HuRo, a dataset of 630K robotized episodes derived from diverse human videos, created via a pipeline that aligns observations and actions for robotic use. Experiments on four real‑world manipulation tasks show that scaling robotized pretraining boosts task completion from 51.5% to 80.3% and improves out‑of‑distribution performance under spatial and visual shifts. Ablation studies reveal that visual robotization enhances robustness and that end‑to‑end pretraining with retargeted actions outperforms visual‑only transfer.

Scaling Mixture-of-Experts Video Pretraining for Embodied Intelligence

Despite the recent promise in robot control, video generative models suffer from a domain mismatch due to their primary focus on content creation. For example, their design inherently prioritizes visual fidelity and creativity over computational efficiency and physical realism.

Direction-Scale Decomposition in Action Representation: Rethinking What to Tokenize for Vision-Language-Action Models

The paper introduces Direction-Scale Decomposition (DSD), an action representation that separates translation and rotation increments into direction and scale components before tokenization. DSD is evaluated with uniform binning and a B-spline tokenizer (BEAST) in both simulation and real-world manipulation tasks, showing improved success rates on LIBERO and SimplerEnv, especially under mixed-dataset training. Real-robot experiments confirm performance gains with and without robotics pretraining, supporting DSD as an effective representation for discrete-token vision-language-action models.

Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine‑Tuning, and On‑Device Optimizations

Build awesome datasets for video generation

What Matters When Cotraining Robot Manipulation Policies on Everyday Human Videos?

arXiv:2606. 06627v1 Announce Type: cross Abstract: Human video datasets used for cotraining robot manipulation policies largely consist of curated demonstrations where motions are orchestrated to resemble robot behavior and 3D hand poses are captured with specialized hardware.