  Learning generalizable visual representations from Internet data has yielded
promising results for robotics. Yet, prevailing approaches focus on
pre-training 2D representations, being sub-optimal to deal with occlusions and
accurately localize objects in complex 3D scenes. Meanwhile, 3D representation
learning has been limited to single-object understanding. To address these
limitations, we introduce a novel 3D pre-training framework for robotics named
SUGAR that captures semantic, geometric and affordance properties of objects
through 3D point clouds. We underscore the importance of cluttered scenes in 3D
representation learning, and automatically construct a multi-object dataset
benefiting from cost-free supervision in simulation. SUGAR employs a versatile
transformer-based model to jointly address five pre-training tasks, namely
cross-modal knowledge distillation for semantic learning, masked point modeling
to understand geometry structures, grasping pose synthesis for object
affordance, 3D instance segmentation and referring expression grounding to
analyze cluttered scenes. We evaluate our learned representation on three
robotic-related tasks, namely, zero-shot 3D object recognition, referring
expression grounding, and language-driven robotic manipulation. Experimental
results show that SUGAR's 3D representation outperforms state-of-the-art 2D and
3D representations.
