  Language-Guided Robotic Manipulation (LGRM) is a challenging task as it
requires a robot to understand human instructions to manipulate everyday
objects. Recent approaches in LGRM rely on pre-trained Visual Grounding (VG)
models to detect objects without adapting to manipulation environments. This
results in a performance drop due to a substantial domain gap between the
pre-training and real-world data. A straightforward solution is to collect
additional training data, but the cost of human-annotation is extortionate. In
this paper, we propose Grounding Vision to Ceaselessly Created Instructions
(GVCCI), a lifelong learning framework for LGRM, which continuously learns VG
without human supervision. GVCCI iteratively generates synthetic instruction
via object detection and trains the VG model with the generated data. We
validate our framework in offline and online settings across diverse
environments on different VG models. Experimental results show that
accumulating synthetic data from GVCCI leads to a steady improvement in VG by
up to 56.7% and improves resultant LGRM by up to 29.4%. Furthermore, the
qualitative analysis shows that the unadapted VG model often fails to find
correct objects due to a strong bias learned from the pre-training data.
Finally, we introduce a novel VG dataset for LGRM, consisting of nearly 252k
triplets of image-object-instruction from diverse manipulation environments.
