Pose Control of a Multi-Segment Soft Continuum Robot Based on HR-DP Algorithm

Soft continuum robots have broad application prospects due to their superior flexibility, compliance and safety. However, the high mechanical complexity addresses a major challenge in precise pose control modeling. Deep Deterministic Policy Gradient (DDPG) algorithm, as a foundational modelfree, off-policy method, is well-suited for such tasks. However, DDPG is known for its inherent instabilities, particularly its susceptibility to Q-value overestimation and challenges in sparse reward environments, which can hinder performance and efficiency. To address these limitations, this paper proposes a Hybrid Reward-Shaped DDPG and PSO fusion algorithm(HR-DP). This method integrates a multi-component reward function with the PSO algorithm, aiming to guide the agent's exploration more effectively and higher exploration accuracy. The target point tracking experiment by the two-segment soft continuum robot in simulation was performed. The results demonstrate that the robot controlled by HR-DP can achieve significantly faster convergence, higher cumulative rewards, higher accuracy and greater stability than that by the standard DDPG baseline and other state-of-theart methods. This work provides a practical and effective HR-DP algorithm enhanced from DDPG, which can benefit pose control of the multi-segment soft continuum robot in complex, real-world environment.