arXiv AI
RoboCurate: Harnessing Diversity with Action-Verified Synthetic Data for Robot Learning
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RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis
arXiv:2606. 28385v1 Announce Type: cross Abstract: Recent advances in robot world models enable synthetic video generation for embodied prediction and planning.
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Video Prediction Policy 2: Predict Better, Act Better
Video Prediction Policy 2 (VPP2) is a new world action model that improves zero‑shot generalization for both video prediction and action generation. It achieves this by pretraining a large, diverse manipulation video dataset with event‑level supervision, then distilling the model into a single‑step visual planner and adding a mixture‑of‑transformers action module. Experiments show VPP2 outperforms leading baselines on open‑ended video prediction, real‑world zero‑shot manipulation, and several challenging benchmarks.
arXiv AI
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RoboTryOn: Synthesizing Dexterous Robot Demonstrations from Human Videos via Virtual Try-On
arXiv:2611.05163v2 Announce Type: replace-cross Abstract: Robot foundation models have shown remarkable performance in robot control, yet adapting them to a new task still relies heavily on teleopera...
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InternW0-$\Delta$: A World Action Model Bridging Predictive Dynamics and Actions with 20K+ Hours of Open Data
InternW0-Δ is a unified World Action Model that integrates pretrained visual dynamics, scene semantics, 4D geometry, and motion priors within a Mixture-of-Transformers framework to generate robot actions. It leverages a frozen VLM for semantic guidance, a 4D foundation model for geometric priors, and introduces Causal Imprint to learn future-relevant scene changes without future-video rollout. The model is pretrained on a newly curated 20K‑hour heterogeneous corpus of robot and human demonstrations, achieving superior performance on simulation benchmarks and real‑robot platforms.
arXiv AI
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Synthesizing Dexterous Robot Demonstrations from Human Videos via Virtual Try-On
The paper introduces RoboTryOn, a method that transforms human egocentric videos into synthetic demonstrations for robots by virtually swapping the human with a target robot while preserving task, objects, and background. It uses autoregressive keyframe translation guided by human hand skeletons and an inverse dynamics model to generate action labels for policy learning. Experiments on various humanoid robots show that policies trained on these synthetic videos achieve up to 60% success on new tasks, matching teleoperated demonstration performance at roughly one‑third the cost.
arXiv Computer Vision
Sep 30
Counterfactual Video Generation Enables Scalable Humanoid Loco-Manipulation
arXiv:2609.38172v1 Announce Type: cross Abstract: Teaching humanoids loco-manipulation skills, such as carrying diverse objects, via visual imitation is a promising path toward generalist robots. How...