Improving Robotic Manipulation Robustness via NICE Scene Surgery

Understand

Learning robust visuomotor policies for robotic manipulation remains a challenge in real-world settings, where visual distractors can significantly degrade performance and safety.

• In this work, we propose an effective and scalable framework, Naturalistic Inpainting for Context Enhancement (NICE).
• Our method minimizes out-of-distribution (OOD) gap in imitation learning by increasing visual diversity through construction of new experiences using existing demonstrations.
• By utilizing image generative frameworks and large language models, NICE performs three editing operations, object replacement, restyling, and removal of distracting (non-target) objects.