Soft wearable robotic gloves based on tendon-sheath mechanism are widely developed for assisting people with a loss of hand mobility. For these robots, knowing the fingertip
forces applied to deformable objects is crucial in successfully grasping them without causing excessive deformations. Existing studies presented methods to predict fingertip force applied to rigid objects only using information from the actuation system. However, forces applied to deformable objects are subject to non-linearity and hysteresis in relation to the objects’ stiffness, which further complicates the problem. We develop a deep-learning model that can accurately estimate the fingertip forces applied to deformable objects using motor
encoder values, motor current, and wire tension. Our model is based on an integrated system of Long Short-Term Memory models that 1) estimates stiffness of the grasped objects and 2)
incorporates the estimated stiffness for predicting the fingertip forces.
Related publications
1. E Rho, D Kim, H Lee, S Jo, Learning Fingertip Force to Grasp Deformable Objects for Soft Wearable Hand Robot with TSM, IEEE Robotics and Automation Letters (presented in IROS2021), 6:4, 8126-8133, Oct 2021 [LINK] [PDF] [VIDEO]
