In this paper we propose an exoskeleton control method for adaptive learning of assistive joint torque profiles in periodic tasks. We use human muscle activity as feedback to adapt the assistive joint torque behaviour in a way that the muscle activity is minimised. The user can then relax while the exoskeleton takes over the task execution. If the task is altered and the existing assistive behaviour becomes inadequate, the exoskeleton gradually adapts to the new task execution so that the increased muscle activity caused by the new desired task can be reduced. The advantage of the proposed method is that it does not require biomechanical or dynamical models. Our proposed learning system uses Dynamical Movement Primitives (DMPs) as a trajectory generator and parameters of DMPs are modulated using Locally Weighted Regression. Then, the learning system is combined with adaptive oscillators that determine the phase and frequency of motion according to measured Electromyography (EMG) signals. We tested the method with real robot experiments where subjects wearing an elbow exoskeleton had to move an object of an unknown mass according to a predefined reference motion. We further evaluated the proposed approach on a whole-arm exoskeleton to show that it is able to adaptively derive assistive torques even for multiple-joint motion.

This paper presents a new research platform, CB 2 , a child robot with biomimetic body for cognitive developmental robotics [1] developed by the Socially-Synergistic Intelligence (Hereafter, Socio-SI) group of JST ERATO Asada Project. The Socio-SI group has focused on the design principles of communicative and intelligent machines and human social development through building a humanoid robot that has physical and perceptual structures close to us, that enables safe and close interactions with humans. For this purpose, CB 2 was designed, especially in order to establish and maintain a long-term social interaction between human and robot. The most significant features of CB 2 are a whole-body soft skin (silicon surface with many tactile sensors underneath) and flexible joints (51 pneumatic actuators). The fundamental capabilities and the preliminary experiments are shown, and the future work is discussed.

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