Proprioceptive and Exteroceptive Information Perception in a Fabric Soft Robotic Arm via Physical Reservoir Computing with Minimal Training Data

Over recent decades, soft and reconfigurable robots have rapidly emerged thanks to their ability to interact safely with humans and adapt to complex environments. . However, their softness makes accurate control challenging, requiring high‐fidelity sensing for posture and contact estimation.Traditional camera‐based sensors and load cells have limited portability and accuracy, and they will inevitably increase the robot's cost and weight. In this study, instead of using specialized sensors, only distributed pressure data inside a pneumatics‐driven soft arm are collected and the physical reservoir computing principle is applied to simultaneously predict its kinematic posture (i.e., bending angle) and payload status (i.e., payload mass). Results show that, with careful readout training, one can obtain accurate bending angle and payload mass predictions via simple, weighted linear summations of pressure readings. In addition,analysis show that, to guarantee low prediction errors within 10%, bending angle prediction requires less training data than payload prediction. This reveals that balanced linear and nonlinear body dynamics are critical for the physical reservoir to accomplish complex proprioceptive and exteroceptive information perception tasks. Finally, the method of exploring efficient readout training methods presented here could be extended to other soft robotic systems to maximize their perception capabilities.