Learning effective visuomotor policies for robotic manipulation is challenging, as it requires generating precise actions while maintaining computational efficiency. Existing methods remain unsatisfactory due to inherent limitations in the essential action representation and the basic network architectures. We observe that representing actions in the frequency domain captures the structured nature of motion more effectively: low-frequency components reflect global movement patterns, while high-frequency components encode fine local details. Additionally, robotic manipulation tasks of varying complexity demand different levels of modeling precision across these frequency bands. Motivated by this, we propose a novel paradigm for visuomotor policy learning that progressively models hierarchical frequency components. To further enhance precision, we introduce continuous latent representations that maintain smoothness and continuity in the action space. Extensive experiments across diverse 2D and 3D robotic manipulation benchmarks demonstrate that our approach outperforms existing methods in both accuracy and efficiency, showcasing the potential of a frequency-domain autoregressive framework with continuous tokens for generalized robotic manipulation.Code is available at https://github.com/4DVLab/Freqpolicy

Regarding to target-driven visual navigation problem, training a universal value function or policy function approximator is considered to be a fairly difficult task, because there may exits potential conflicts among different targets. When modeling navigation as a goal-conditional reinforcement learning problem, the algorithm can only support a relatively small number of goals, which limits the universality of the reinforcement learning based methods. In this work, we proposed a framework for multi-targets visual navigation, termed Joint Critics Mechanism, to better train the universal policy function approximator. Recognizing that target-specific network has better convergence, we use the target-specific value network to estimate the advantage of the target-universal policy network for better convergence. In this way, we avoid complexity of learning competitive targets and achieve a better convergence with a larger number of targets. For evaluation, we conduct experiments in realistic simulation environments and the results prove the rationality and effectiveness of our proposed framework.