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With the advent of large language models and large-scale robotic datasets, there has been tremendous progress in high-level decision-making for object manipulation [1, 2, 3, 4]. These generic models are able to interpret complex tasks using language commands, but they often have difficulties generalizing to out-of-distribution objects due to the inability of low-level action primitives. In contrast, existing task-specific models [5, 6] excel in low-level manipulation of unknown objects, but only work for a single type of action. To bridge this gap, we present M2T2, a single model that supplies different types of low-level actions that work robustly on arbitrary objects in cluttered scenes. M2T2 is a transformer model which reasons about contact points and predicts valid gripper poses for different action modes given a raw point cloud of the scene. Trained on a large-scale synthetic dataset with 128K scenes, M2T2 achieves zero-shot sim2real transfer on the real robot, outperforming the baseline system with state- of-the-art task-specific models by about 19% in overall performance and 37.5% in challenging scenes where the object needs to be re-oriented for collision- free placement. M2T2 also achieves state-of-the-art results on a subset of language conditioned tasks in RLBench [7]. Videos of robot experiments on unseen objects in both real world and simulation are available on our project website https://m2-t2.github.io.