The field of robotics is witnessing significant advancements in manipulation and locomotion capabilities. Researchers are exploring innovative approaches to enable robots to learn and adapt to new tasks and environments, with a focus on developing more robust and generalizable policies. One of the key trends is the use of diffusion-based models and reinforcement learning to improve the efficiency and effectiveness of robotic learning. Another area of interest is the development of whole-body control frameworks for humanoid robots, which aims to enhance their balance and stability in dynamic environments. Furthermore, there is a growing emphasis on leveraging tactile modalities and multimodal sensing to improve manipulation capabilities. Noteworthy papers in this area include mimic-one, which presents a scalable model recipe for real-world control of a highly dexterous humanoid robotic hand, and SAIL, which introduces a novel approach for faster-than-demonstration execution of imitation learning policies. Additionally, papers like DynaGuide and Latent Action Diffusion demonstrate the potential of diffusion policies and latent action spaces for improving robotic manipulation and cross-embodiment learning.