  Robot manipulation policies have shown unsatisfactory action performance when
confronted with novel task or object instances. Hence, the capability to
automatically detect and self-correct failure action is essential for a
practical robotic system. Recently, Multimodal Large Language Models (MLLMs)
have shown promise in visual instruction following and demonstrated strong
reasoning abilities in various tasks. To unleash general MLLMs as an end-to-end
robotic agent, we introduce a Self-Corrected (SC)-MLLM, equipping our model not
only to predict end-effector poses but also to autonomously recognize and
correct failure actions. Specifically, we first conduct parameter-efficient
fine-tuning to empower MLLM with pose prediction ability, which is reframed as
a language modeling problem. When facing execution failures, our model learns
to identify low-level action error causes (i.e., position and rotation errors)
and adaptively seeks prompt feedback from experts. Based on the feedback,
SC-MLLM rethinks the current failure scene and generates the corrected actions.
Furthermore, we design a continuous policy learning method for successfully
corrected samples, enhancing the model's adaptability to the current scene
configuration and reducing the frequency of expert intervention. To evaluate
our SC-MLLM, we conduct extensive experiments in both simulation and real-world
settings. SC-MLLM agent significantly improve manipulation accuracy compared to
previous state-of-the-art robotic MLLM (ManipLLM), increasing from 57\% to 79\%
on seen object categories and from 47\% to 69\% on unseen novel categories.
