The field of robotic manipulation is witnessing significant developments, with a focus on improving efficiency, robustness, and adaptability. Researchers are exploring innovative approaches to garment manipulation, imitation learning, and diffusion policies, which are enhancing the capabilities of robots in various tasks. Notably, the incorporation of standardization, equivariance, and multimodal perception is leading to improved performance and generalization in robotic manipulation. These advancements have the potential to transform various applications, including household tasks, molecular dynamics, and human-robot interaction. Noteworthy papers include:

• Learning Efficient Robotic Garment Manipulation with Standardization, which introduces APS-Net, a novel approach that combines unfolding and standardization for efficient garment manipulation.
• SE(3)-Equivariant Diffusion Policy in Spherical Fourier Space, which proposes an SE(3) equivariant diffusion policy that adapts trajectories according to 3D transformations of the scene, demonstrating robust generalization across transformed 3D scenes.
• AC-DiT: Adaptive Coordination Diffusion Transformer for Mobile Manipulation, which enhances mobile base and manipulator coordination for end-to-end mobile manipulation using a mobility-to-body conditioning mechanism and perception-aware multimodal conditioning strategy.