In recent years, robots with higher levels of sophistication in controlling the mechanical impedance of interaction have received increasing attention. These robots can precisely move and manipulate objects in predetermined situations by exploiting impedance control. However, this requires that the desired impedance of interaction be factored into the control design process beforehand, and interaction impedance is typically not dynamically modulated traditionally. In contrast, humans are skilled at modulating mechanical impedance, mainly mechanical stiffness, in their interactions with their surroundings. This ability is vital for successful interaction with the environment. This human behaviour has inspired various variable impedance learning controllers for tool manipulation. However, humans not only demonstrate the ability to stabilise interaction with a divergent force-field but also demonstrate a few higher-level learning features. Savings is one such feature, and it refers to people being able to learn faster when the same divergent-field is applied again. Retention is another essential feature, allowing humans to initially demonstrate lower error when the same exposure to a divergent field (perturbation) is applied again. These features have practical utility in the robotic application and have not been explored, this paper presents a novel approach to variable impedance learning control for robots inspired by human motor learning mechanisms. We combine iterative learning control, feedforward control, and ideas from neuroscience to synthesise the controller. The proposed method aims to vary impedance to stabilise the interaction with divergent-field while incorporating savings and retention features. Simulation results with a two-link serial chain manipulator demonstrate the proposed method’s efficacy, savings, and retention.

The future of robotics involves a growing need for robots to perform everyday tasks in dynamic and unstructured environments. This often requires them to handle complex and unstable interactions with various objects, including mechanical tools, flexible objects such as food items and fruits, and more. Robots must be able to interact with their environment like humans, who rely on the compliance of their musculoskeletal system to achieve safe and effective interaction. Previous research has analyzed the benefits of incorporating mechanical compliance in parallel and series configurations to address this requirement. In this work, we introduce a novel approach to parallel compliance, where the mean position is adjusted based on desired trajectories and study its advantages. The unstable interactions are modeled as a position-based divergent force field. A generalized theory is presented that determines the minimum gains required for trajectory tracking in all types of compliance configurations in the presence of divergent force fields. The results are demonstrated through simulations of 1-DOF and 2-DOF robots and provides insight into the associated control effort.

Thumb degree-of-freedom (DOF) allocation in anthropomorphic robot hands involves a trade-off between functional mobility and mechanical-control complexity. This study presents a controlled multi-metric framework for comparing recurring thumb DOF configurations under common palm geometry, non-thumb finger structure, reference frames, Denavit–Hartenberg kinematics, and sampling assumptions. Five literature-derived thumb configurations, namely 3-1-1, 2-2-1, 2-1-1, 2-0-1, and 1-1-1, were evaluated to determine which thumb DOFs should be preserved when kinematic complexity is reduced. The theoretical evaluation included Kapandji Opposition Test reachability, opposition alignment, workspace volume, workspace compactness, cylindrical grasp opportunity, and Jacobian-based dexterity. A targeted experimental validation of the 2-1-1 and 2-0-1 prototypes was then performed on a tendon-driven test bench. The results showed that qualitatively similar thumb configurations are quantitatively unequal: several designs achieved identical Kapandji scores but differed substantially in workspace, alignment, dexterity, and grasp feasibility. Overall, 3-1-1 achieved the strongest overall capability, while 2-2-1 emerged as the strongest reduced-complexity alternative and achieved the best mean dexterity. Retaining two active carpometacarpal DOFs preserved a large share of dexterous function, whereas metacarpophalangeal fixation maintained selected cylindrical grasps but narrowed the feasible task boundary.

Robotics-based solutions often rely on motors, and the precision and accuracy of these robots depend on the quality of their motion control. The most common type of motor is the DC motor, and two approaches for controlling DC motors are available: feedback and feedforward control. Inversion-based feedforward control, the most straightforward approach, uses the system model to provide input. However, this approach is not suitable when system parameters change over time. Feedback control, on the other hand, does not rely on the system model and provides control input based on the error signal. However, feedback control performs poorly in the presence of noise in the feedback signal. In this study, we propose a novel approach combining feedback and feedforward control for DC motor control. We use a smooth switching law that selects the contribution of feedback and feedforward control based on the variance of the error and estimation gain for time-varying systems. Extensive simulation results demonstrate that our approach of smooth switching between feedforward and feedback control provides superior performance in tracing, compared to LQR, in the presence of noisy feedback.