Soft wearable hand robots with tendon-sheath mechanisms are being actively developed to assist people with lost hand mobility. For these robots, accurately estimating fingertip forces leads to successful object grasping. An approach can utilize information from actuators assuming quasi-static environments. However, non-linearity and hysteresis with regards to the dynamic changes of the tendon-sheath mechanism hinder accurate fingertip force estimation. We propose a learning-based method to estimate fingertip forces by integrating dynamic information of motor encoders, wire tension, and sheath bending angles. The model is modified from Long Short-Term Memory by incorporating a residual term that governs the dynamic changes in sheath bending angles. For this work, we collaborate with Prof. Kyu-Jin Cho’s group at Seoul National University.


Related publications

1. B Kim, D Kim, H Choi, U Jeong, K Kim, S Jo, K-J Cho, Learning-based fingertip force estimation for soft wearable hand robot with tendon-sheath mechanism, IEEE RA-L, 5(2),946-953, 2020 (presented in ICRA 2020). [LINK][PDF]