Human gait can be measured using a change in the joint angles value of the lower limb joints during walking. Inverse kinematics (IK) of a human leg refers to calculating joint angle values from different leg positions during locomotion. This paper calculates a real-time IK for a 3-link kinematic leg using a musculoskeletal model in OpenSim. The model provides the analytical solution of IK, which is quick and accurate. But, the model-based approach is very much dependent on the morphology of the bipedal robot. The existing bipedal robots are mainly planned for walking on flat terrain and unconstrained environment. So, these limitations lead to the developing of a generalised polynomial equation for generating walking trajectories. The model-based approaches rely on the degree of similarity between the virtual human model and the actual human body, which does not fully describe the human body. Due to the difference in real and virtual models, the model-based approaches suffer during the optimisation process of the boundary conditions and the mass-inertial parameters, leading to an unstable numerical solution. The learning-based method using long short-term memory (LSTM) model for gait generation is proposed to overcome the limitation of model-based approaches.

In this study, we present a novel Shape Memory Alloy (SMA)-actuated robotic hand which can provide a higher level of dexterity and compliance. The mechanical structure and actuation of the robotic hand are designed similar to the musculoskeletal system of a human hand. SMAs are used in a similar manner where muscles and ligaments are applied in a human hand. In our design, the compliance of SMAs are used to manipulate soft, fragile, and/or rigid objects more adaptively and naturally. In particular, SMA actuators can provide a high power-to-weight ratio, which is preferable for a compact and lighter design. Each finger joint in the hand is actuated independently. Thus, the robotic hand can achieve dexterous in-hand manipulation. The grasping and in-hand manipulation experiments verify the robot design using open-loop control and closed-loop control, respectively.