The fields of robotics and artificial intelligence are rapidly evolving, with significant advancements in recent years. One of the common themes among these advancements is the development of more efficient, adaptive, and robust systems. In the field of quadrotor research, innovative approaches such as adaptive MARG-only heading estimation and rotor-failure-aware navigation are being explored to improve the accuracy and reliability of quadrotor systems. The use of speculative decoding in large language model inference is also gaining attention, with techniques such as adaptive rescheduling and dynamic speculative sampling being proposed to optimize the process. Additionally, the development of controllable world models and novel attention mechanisms is enhancing the ability of robots to evaluate and improve their policies. Other notable advancements include the introduction of neuro-inspired approaches for manipulation, the development of gripper-aware grasp detection frameworks, and the use of tactile sensing to improve robotic grasping and manipulation. The integration of uncertainty-driven foresight, classifier-free guidance, and vision-language models is also enhancing the performance and generalization capabilities of robotic systems. Overall, these advancements have the potential to significantly improve the autonomy and effectiveness of robots in various applications, including robotic manipulation, autonomous excavation, and assistive feeding. Some noteworthy papers in these areas include AMO-HEAD, Rotor-Failure-Aware Quadrotors Flight, Conformal Sparsification for Bandwidth-Efficient Edge-Cloud Speculative Decoding, Mirror Speculative Decoding, OmniSAT, Ctrl-World, Actron3D, CacheClip, UltraLLaDA, EAGER, Catch Your Breath, Simultaneous Calibration of Noise Covariance and Kinematics for State Estimation of Legged Robots, Proprioceptive Image, Towards Dynamic Quadrupedal Gaits, Residual MPC, Architecture Is All You Need, SpikeGrasp, XGrasp, Learning to Grasp Anything by Playing with Random Toys, Cross-Sensor Touch Generation, Learning to Throw-Flip, Refinery, LMCache, Zephyrus, FlexPipe, KVCOMM, BanaServe, xLLM, UF-RNN, Phys2Real, High-Fidelity Simulated Data Generation for Real-World Zero-Shot Robotic Manipulation Learning, Population-Coded Spiking Neural Networks for High-Dimensional Robotic Control, DemoHLM, and Open TeleDex.