In the field of robotics, researchers have frequently exploited geometric and physical structures to simplify complex planning and control problems. One such natural geometric structure is symmetry. Recently, equivariant models, which encode symmetries within the neural network architecture, have achieved remarkable success in the machine learning community. This raises the question of how these geometric deep learning techniques can be effectively applied to robotics. Our recent work has taken a step in this direction by applying equivariant learning methods to robotic manipulation, aiming to enhance sample efficiency and generalization. Our findings reveal that these methods significantly outperform conventional unconstrained models and effectively enable on-robot reinforcement learning.