A data acquisition system was developed to collect human joint angle data, which served as a prior knowledge input for DRL, thereby optimizing the reward function of the deep deterministic policy gradient (DDPG) algorithm and improving the learning stability and convergence speed. This approach was successfully applied in the humanoid robot BHR-6, enabling it to perform tasks that are challenging to train using existing methods. [PERSON] et al. [12] proposed an end-to-end navigation strategy based on causal reinforcement learning, which is learned directly from the data, bypassing the traditional mapping and planning steps, thereby improving the responsiveness of drones in obstacle avoidance and navigation tasks. To address the issue of local minima in robotic arm collision avoidance using Artificial Potential Field (APF), a study proposed a method that integrates APF with deep reinforcement learning (DRL) and improves Hindsight Experience Replay (HER) into HER-CA to enhance training efficiency [13]. [PERSON] et al. [14] proposes a decision reconstruction network integrated into the SAC algorithm. This network dynamically reconfigures network layers to enable cross-layer information exchange, effectively mitigating gradient conflicts and addressing the optimization challenges caused by parameter-sharing in robotic multi-task reinforcement learning. [PERSON] et al. [15] examined regularization-based methods like synaptic intelligence for sequential learning in offline RL, finding they reduce learning time and mitigate forgetting but offer limited knowledge transfer. The study proposed a deep reinforcement learning-based coverage path planning approach, using actor-critic methods to train mobile robots for exploration and full coverage [16]. [PERSON] et al. [17] introduced the Efficient Progressive Policy Enhancement (EPPE) framework, which integrates sparse and process rewards to improve DRL training efficiency and global optimality in path-planning. [PERSON] et al. [18] have used a welding robot path-planning method based on the DDPG algorithm to solve the path-planning problem in a high-dimensional continuous state and action space. [PERSON] et al. [19] proposed a multi-agent reinforcement learning approach for square shaft-hole assembly. The method designs a reward function based on the Decoupled Multi-Agent Deep Deterministic Policy Gradient (DMDDPG) algorithm and models the first three and last three joints of the robotic arm as a multi-agent system. [PERSON] et al. [20] proposed DRL-based path planning method incorporates a safety verification mechanism to achieve collision-free trajectories. However, it still has limitations such as fixed end-effector orientation and the requirement for a smaller threshold in trajectory merging. [PERSON] et al. [21] introduced a method to control a mobile robotic arm to perform door-opening operations based on an improved Proximal Policy Optimization (PPO) algorithm. [PERSON] et al. [22] achieved the requirements of online trajectory-planning by training a strategy consisting of a feedforward neural network in the simulation environment. [PERSON] et al. [23] employed the DDPG algorithm to optimize the static obstacle-avoidance performance of robotic arms; however, they did not extend their approach to dynamic obstacle environments. To address high-dimensional path-planning challenges, [PERSON] et al. [24] proposed a path-planning algorithm based on the Soft Actor-Critic (SAC) framework, utilizing configuration space augmentation to manage the complex configuration spaces associated with multi-arm systems. They also incorporated the Hindsight Experience Replay (HER) technique to enhance the sampling efficiency, enabling the real-time and rapid generation of any end-to-end shortest path. [PERSON] et al. [25] combined recurrent neural networks with temporal attention processes and deep reinforcement learning, allowing agents to remember important contextual data in order to prevent collisions.