ReShoot: Generative Visual Domain Randomization of Recorded Robot Demonstrations for Visuomotor Policy Learning

arXiv:2609.19661 · cs.RO · Submitted 2026-09-17 · Read on arXiv

cs.RO

Submitted: 2026-09-17

Updated: 2026-09-17

Terminology

• Robustness — 13× in this paper · explained in 5 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Distribution Shift — 5× in this paper · explained in 5 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Diffusion Models — 3× in this paper · explained in 6 episodes
• World Model — 3× in this paper · explained in 7 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Structural Control — 2× in this paper · explained in 2 episodes
• Action Chunking — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• VLA Models — 1× in this paper · explained in 3 episodes

Sources

• CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation Learning
• Wan: Open and Advanced Large-Scale Video Generative Models
• Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control
• Green Screen Augmentation Enables Scene Generalisation in Robotic Manipulation

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