Multi-Task Estimation of Tip Kinematics and External Force in a Continuum Robot Using Fused Proprioceptive Sensing

Kinematic modeling of soft continuum robots in constrained environments remains challenging due to their complex nonlinear dynamics. Data-driven processing of proprioceptive signals offers a promising pathway to enhance robot perception of both its own state and the external environment. This paper proposes a soft continuum robot structure with multiple integrated proprioceptive sensing, including measurements of tendon tension and intersegmental pressure. A long short-term memory (LSTM) network is employed to jointly estimate multiple perception tasks, including tip position, orientation, and the magnitude and direction of external forces. Ablation studies demonstrate that fused proprioceptive inputs yield significantly higher accuracy in multi-task estimation than single-modality inputs. The proposed method provides a novel and effective approach for advancing perception and control in soft continuum robotics.