  Robot manipulation relies on accurately predicting contact points and
end-effector directions to ensure successful operation. However, learning-based
robot manipulation, trained on a limited category within a simulator, often
struggles to achieve generalizability, especially when confronted with
extensive categories. Therefore, we introduce an innovative approach for robot
manipulation that leverages the robust reasoning capabilities of Multimodal
Large Language Models (MLLMs) to enhance the stability and generalization of
manipulation. By fine-tuning the injected adapters, we preserve the inherent
common sense and reasoning ability of the MLLMs while equipping them with the
ability for manipulation. The fundamental insight lies in the introduced
fine-tuning paradigm, encompassing object category understanding, affordance
prior reasoning, and object-centric pose prediction to stimulate the reasoning
ability of MLLM in manipulation. During inference, our approach utilizes an RGB
image and text prompt to predict the end effector's pose in chain of thoughts.
After the initial contact is established, an active impedance adaptation policy
is introduced to plan the upcoming waypoints in a closed-loop manner. Moreover,
in real world, we design a test-time adaptation (TTA) strategy for manipulation
to enable the model better adapt to the current real-world scene configuration.
Experiments in simulator and real-world show the promising performance of
ManipLLM. More details and demonstrations can be found at
https://sites.google.com/view/manipllm.
