Concentric tube continuum robots (CTCRs) belong to the family of continuum robots with applications in minimally invasive surgeries. Because of this application domain, measuring the external forces along the body of the robot is paramount. CTCRs are made up of thin elastic rods and are intended to be applied inside the human body, where conventional sensor-based measurements are not feasible. Consequently, research is resorting to estimate the forces through geometric, numeric, or optimization methods. However, these methods often suffer from slow convergence. In this paper, we introduce a novel data-driven approach for estimating contact forces along the body of a CTCR that offers an estimation precision comparable to the current state-of-the-art optimization-based approaches, but exhibits nearly two orders of magnitude faster convergence. The proposed method is scalable and exhibits a significant performance in response to a wide range of external forces. The approach was evaluated in simulations and on a real 2-tube CTCR.

We propose a novel hierarchical online learning scheme for fast and efficient bootstrapping of sensorimotor skills. Our scheme permits rapid data-driven robot model learning in a "learning while behaving" fashion. It is updated continuously to adapt to time-dependent changes and driven by an intrinsic motivation signal. It utilizes an online associative radial basis function network, which is the first associative dynamic network to be constructed from scratch with high stability. Moreover, we propose a parameter-sharing technique to increase efficiency, stabilize the online scheme, avoid exhaustive parameter tuning, and speed up the learning process. We apply our proposed algorithms on a 7-DoF physical robot manipulator and demonstrate their performance and efficiency.