While lower-limb exoskeletons have been increasingly used for gait assistance and rehabilitation, most of them continue to function as assistive devices in the exoskeleton-user relationship as a leader and follower. This limits the user’s ability to interactively contribute to gait control. Therefore, this study proposes an interactive user-exoskeleton control strategy to translate the exoskeletons into interactive compliant companion devices with the exoskeleton-user relationship as the collaborator. This strategy is implemented through online adaptive impedance control with gravity compensation (OAIC-GC). It relies solely on internal pose feedback (joint position) rather than external sensors such as electromyography, torque, or force, as utilized in other assist-as-needed (AAN) control methods. The OAIC-GC can automatically capture the mechanical impedance dynamics of the user’s lower limbs during walking and thus facilitate adaptive, versatile, and personalized gait assistance. It is evaluated using a real lower-limb exoskeleton system with six degrees of freedom (DOFs) across different users engaging in various activities. These activities include symmetrical and asymmetrical walking on a split-belt treadmill at different speeds, as well as walking up stairs. The results indicate a significant improvement in the exoskeleton’s performance in terms of adaptability and movement smoothness under all activities when compared to traditional control. The proposed control reduces joint assistance torque across all exoskeleton joints, enhancing user interaction and comfort. This enables users to actively control their gait patterns, enabling the exoskeleton to operate in an interactive assist-as-needed (IAAN) mode.

Bayesian filters have been considered to help refine and develop theoretical views on spatial cell functions for self-localization. However, extending a Bayesian filter to reproduce insect-like navigation behaviors (e.g., home searching) remains an open and challenging problem. To address this problem, we propose an embodied neural controller for self-localization, foraging, backward homing (BH), and home searching of an advanced mobility sensor (AMOS)-driven insect-like robot. The controller, comprising a navigation module for the Bayesian self-localization and goal-directed control of AMOS and a locomotion module for coordinating the 18 joints of AMOS, leads to its robust insect-like navigation behaviors. As a result, the proposed controller enables AMOS to perform robust foraging, BH, and home searching against various levels of sensory noise, compared to conventional controllers. Its implementation relies only on self-localization and heading perception, rather than global positioning and landmark guidance. Interestingly, the proposed controller makes AMOS achieve spiral searching patterns comparable to those performed by real insects. We also demonstrated the performance of the controller for real-time indoor and outdoor navigation in a real insect-like robot without any landmark and cognitive map.

Interactive (mechanical) impedance and finger fatigues are important topics, which have not been well investigated. To tackle this problem, we developed a soft lightweight (0.25 kg) finger exoskeleton (TIE-EXO) for quantifying interactive impedance and finger fatigue. A resist-as-needed (RAN) controller was used to produce variable resistance in fingers’ exercises. The TIE-EXO’s feedback and RAN’s parameters were applied to quantify the relationship between interactive impedance and finger fatigue. This quantification was validated in the index and middle fingers of three subjects. This validation shows that the RAN control enables the TIE-EXO to produce online resistance adaptations to different subjects and finger fatigue. Moreover, it indicates a variation and invariance in finger impedance control. We argue that the proposed method provides a novel way for investigating interactive impedance and finger fatigue.

Manipulation of flexible objects is one of the major challenges in robotics as the nonlinear dynamics of the high-dimensional object structure makes it difficult to apply current control methods. A previous simulation study showed that control with few pre-structured joint trajectories coupled with joint impedance (dynamic primitives) could control a 25-dimensional whip to hit a target. This was possible even though the impedance values were constant. This paper explores whether time-varying impedance throughout the movement may further enhance performance. We present an online impedance adaptation (OIA) controller that modulates the joint impedances of a two-joint actuator in real time for the same task. Results showed that the OIA control method increased the speed of optimization and resulted in smaller deviation from the zero-torque joint trajectories compared to the controller with constant joint impedances. This novel way to modulate both motion and impedance of a manipulator may facilitate the control of flexible objects with significant dynamics.

The control of multilegged animal walking is a neuromechanical process, and to achieve this in an adaptive and energy efficient way is a difficult and challenging problem. This is due to the fact that this process needs in real time: 1) to coordinate very many degrees of freedom of jointed legs; 2) to generate the proper leg stiffness (i.e., compliance); and 3) to determine joint angles that give rise to particular positions at the endpoints of the legs. To tackle this problem for a robotic application, here we present a neuromechanical controller coupled with sensorimotor learning. The controller consists of a modular neural network for coordinating 18 joints and several virtual agonist-antagonist muscle mechanisms (VAAMs) for variable compliant joint motions. In addition, sensorimotor learning, including forward models and dual-rate learning processes, is introduced for predicting foot force feedback and for online tuning the VAAMs' stiffness parameters. The control and learning mechanisms enable the hexapod robot advanced mobility sensor driven-walking device (AMOS) to achieve variable compliant walking that accommodates different gaits and surfaces. As a consequence, AMOS can perform more energy efficient walking, compared to other small legged robots. In addition, this paper also shows that the tight combination of neural control with tunable muscle-like functions, guided by sensory feedback and coupled with sensorimotor learning, is a way forward to better understand and solve adaptive coordination problems in multilegged locomotion.

Tasks planning under uncertainties is one of fundamental skills for enabling autonomous robots to make proper manipulations in the complex environment. But owing to inexpressive representations, autonomous robots hardly conduct efficient tasks planning, especially in unknown conditions. The application of semantic knowledge in task planning is critically required in artificial intelligence research. In this paper, we focus on two topics: semantic knowledge representations and parallel planning for uncertainties. Firstly, a semantic memory system which is called EpistemeBase is proposed for indoor tasks planning, it includes five parallel agents: Assertion, Plan, Anticipation, Behaviour and Effect. Its framework is an evolving process, which consists of Datum, Information, Knowledge and Intelligence. Secondly, the same task planning is synchronously represented by five paralleled agents. This paralleled structure can well accelerate the process of tasks planning as well as better handle it under uncertainties. Finally, the experiment of tasks planning is conducted for measuring the reaction time of planning and uncertainties by using the EpistemeBase and the Open Mind Common Sense (OMCS) respectively.