Teleoperation provides a promising way for human–robot collaboration in the unknown or unstructured environments to perform a cooperative task. It enables humans to complete a task at a remote side and combines both the human's intelligence and the robots’ capabilities in a collaborative task. Therefore, it is necessary to conduct cross researches in terms of robotics, artificial intelligence, sensors, and mechatronics. This study covers the state-of-the-arts research in terms of perception, control, and learning. In this review, key technologies about information perception mechanism, control algorithms, human–robot interface, and robot learning strategies for teleoperation are introduced. Then, a comprehensive survey is summarised in representative applications in teleoperation, and the existing challenges and potential directions of the development are discussed.

A novel variable stiffness soft robotic hand (SRH) consists of three pieces of layer jamming structure (LJS) is proposed. The mechanism is driven by the motor-based tendon along the surface of the pieces that connect to individual gas channel. Each LJS is optimised by adhering a thin layer of hot melt adhesive and overlapping the spring steel sheet as inner layer material. It can be switched between rigid and compliant independently. The structures of variable stiffness and tendon-driven lead to various deformation poses. Then the control system of SRH and the performance analysis of the LJS are introduced. Finally, the experiments are implemented to prove the superiority of the proposed LJS and the demonstrations show that the designed robotic hand has multiple configurations to successfully grasp various objects.

It is hypothesised that specific groups of muscles aka muscle synergies (MSs) are combined by the central nervous system to control a wide repertoire of movements and also simplify motor control. Therefore, studying MSs during human locomotion is of significance, as it may reveal neuromuscular strategies for postural stability. In this study, the authors aimed to use the hypothesis of MSs to identify specific muscle co-activations during overground walking and slacklining where postural perturbations were generated by the participants rather than being externally controlled. Nine participants were asked to walk overground and on a slackline while they recorded myoelectric activity of their leg muscles. They extracted synergies from the electromyography signals in the two tasks using factor analysis. The results showed adaptation in the shared MSs structure during walking on the slackline and these shared MSs across participants were recruited flexibly to meet the demand for stability. The modulation of synergies suggests adaptive neuromuscular strategies for stability while walking on a slackline. Specifically, higher activation of quadriceps during slacklining suggests a crouched gait to facilitate balance. During overground walking, lower leg muscles revealed higher activation compared to slacklining to support a more consistent toe-off during the stance phase.

In this study, a distributed active noise control (DANC) system for spatial noise control in a network of acoustic sensor nodes based on the behavioural traits of felines is presented. An unified strategy based on incremental co-operative learning and cat swarm intelligence is proposed for noise mitigation in spatial region. The hybrid nature of the proposed incremental cat swarm optimisation (ICSO) algorithm provides efficient noise control without prior estimation of multiple secondary paths. In the developed ICSO-based DANC scheme, the individual sensor nodes communicate the intermediate solutions using incremental mode of cooperation to attain overall global noise mitigation over the distributed network. The performance of the proposed ICSO based DANC scheme is validated for tonal, broadband and practical air conditioner noise control test scenarios. Evaluation results show that the proposed system achieves faster convergence with computational efficiency of over 36% and ∼2–9 dB improvement in noise cancellation for different noise cases and acoustic environments over genetic algorithm and particle swarm optimisation based DANC counterparts.