Soft slender robots with high aspect ratios are prone to passive deformation due to gravity, rendering conventional kinematics ineffective and necessitating gravity compensation. Existing solutions often rely on multiple onboard sensors, which increase system complexity and reduce generalizability. This paper presents a bio-inspired real2sim2real framework that enables real-time gravity awareness and proactive joint-level compensation for portable, cable-driven soft slender robots—using only a single inertial measurement unit (IMU) and real-time simulation. In this method, an IMU affixed to the robot base captures orientation changes during quasi-static rotations. Simultaneously, the IMU readings are streamed to a robotic simulation platform established under the SOFA Framework, where the direction of virtual gravity is dynamically updated. To counteract undesired deformation caused by rotated gravity, we use a quadratic programming (QP) solver to continuously compute the necessary joint space motions that actively negate the virtual deformations. These motions are then executed in both the virtual and physical robots, forming a real2sim2real architecture. Experiments validate the effectiveness of the framework, achieving a compensation recovery rate (correlation coefficient) exceeding 99% in static cases, and 94% in low-motion dynamic cases. This novel method enables soft robots to maintain rich state estimation and configuration consistency under varying gravity by leveraging advanced soft body simulation, thereby minimizing reliance on physical sensors. Our method offers a generalizable and scalable solution for gravity-aware control in soft robotics.
Lai et al. (Sat,) studied this question.