Is Online Interaction Necessary for Recovery? A Minimalist Approach to Robust Planning via Perturbation

arXiv:2609.33049 · cs.RO · Submitted 2026-09-27 · Read on arXiv

cs.RO

Submitted: 2026-09-27

Updated: 2026-09-27

Terminology

• Covariate Shift — 15× in this paper · explained in 2 episodes
• Robustness — 10× in this paper · explained in 5 episodes
• Regularization — 9× in this paper · explained in 3 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• Diffusion Policy — 5× in this paper
• Consistency — 4× in this paper · explained in 3 episodes
• Generative AI — 4× in this paper · explained in 2 episodes
• Perturbation-Augmented Recovery Supervision — 4× in this paper
• Action Chunking — 3× in this paper · explained in 1 episode
• Data Augmentation — 3× in this paper · explained in 7 episodes
• Streaming Flow Policy — 3× in this paper
• Diffusion Policy (DP) — 2× in this paper · explained in 2 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• KL Regularization — 2× in this paper · explained in 2 episodes
• Action Chunking with Transformers — 1× in this paper
• Benchmark — 1× in this paper · explained in 2 episodes
• Diffusion Process — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Korea Advanced Institute of Science and Technology — 1× in this paper
• Latent Space — 1× in this paper · explained in 7 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Reward Function — 1× in this paper · explained in 2 episodes

Sources

• Why Does Action Chunking Improve Behavioral Cloning Performance in Robotic Control?
• Refinement-Based Flow Policy Optimization
• KDPE: A Kernel Density Estimation Strategy for Diffusion Policy Trajectory Selection
• Query-Efficient Imitation Learning for End-to-End Autonomous Driving
• Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

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