This paper addresses the challenge of deploying high-Degree-of-Freedom (DoF) humanoid robots in real-world scenarios, a critical issue in robotics. Previous approaches often relied on oversimplified assumptions, limiting robots’ adaptability. This work emphasizes the need for a flexible control framework for operational efficacy and safety. Significant advancements include floating-based kinematics for expanded robot workspace and stability, and a recursive algorithm for the Centroidal Momentum Matrix (CMM), enhancing motion planning. A novel self-collision avoidance algorithm employing boundary spheres and Control Barrier Functions (CBFs) ensures operational safety. A major contribution is the development of a control barrier function-based Quadratic Programming (QP)-based control framework integrating Proportional-Derivative (PD) control for stable walking. The paper simplifies trajectory design by categorizing motion tasks and enhances dynamic stability using a new angular momentum rate change compensation method with force-torque sensor feedback. These improvements mark a substantial leap in humanoid robotics, enhancing efficiency, functionality, and adaptability, and paving the way for their broader integration into human society.

Advances in social robotics have led to increased interest in designing robots that can communicate effectively with humans through nonverbal gestures. One approach to enhancing the naturalness and expressiveness of robot gestures is to incorporate biological motion, which mimics the velocity and acceleration profiles observed in human gestures. This paper examines the use of biological motion for gestural communication by humanoid social robots, focusing on the impact of biological motion on the perceived warmth and effectiveness of robot gestures in fostering engagement while interacting with these robots. The exercise involved implementing the minimum jerk model of biological motion on a Pepper humanoid social robot and conducting user studies to evaluate the impact of biological motion on human–robot interaction. The results show that incorporating biological motion cues can significantly increase the perceived warmth of robot gestures and improve the overall effectiveness of gestural communication, resulting in more natural and engaging human–robot interaction.

Dual-arm robotic manipulators are used in industry and for assisting humans since they enable dexterous handling of objects and more agile and secure execution of pick-and-place, grasping or assembling tasks. In this paper, a nonlinear optimal control approach is proposed for the dynamic model of a dual robotic arm. In the considered application, the dual-arm robotic system has to transfer an object under synchronized motion of its two end-effectors so as to achieve precise positioning and to compensate for contact forces. The dynamic model of this robotic system is formulated while it is proven that the state-space description of the robot’s dynamics is differentially flat. Next, to solve the associated nonlinear optimal control problem, the dynamic model of the dual-arm robot undergoes approximate linearization around a temporary operating point that is recomputed at each time-step of the control method. The linearization relies on Taylor series expansion and on the associated Jacobian matrices. For the linearized state-space model of the dual-arm robot, a stabilizing optimal (H-infinity) feedback controller is designed. This controller stands for the solution to the nonlinear optimal control problem under model uncertainty and external perturbations. To compute the controller’s feedback gains an algebraic Riccati equation is repetitively solved at each iteration of the control algorithm. The stability properties of the control method are proven through Lyapunov analysis. The proposed nonlinear optimal control approach achieves fast and accurate tracking of reference setpoints under moderate variations of the control inputs.

A human eye has about 120 million rod cells and 6 million cone cells. This huge number of light-sensing cells inside a human eye will continuously produce a huge quantity of visual signals that flow into the human brain for daily processing. However, the real-time processing of these visual signals does not cause excessive energy consumption by the human brain. This fact tells us the truth which is to say that human-like vision processes do not rely on complicated and expensive formulas to compute depth, displacement, and colors. On the other hand, the human eye is like a camera with pan-tilt (PT) motions. We all know that in computer vision, each set of PT parameters (i.e., coefficients of pan motion as well as tilt motion) requires a dedicated calibration to determine a camera’s projection matrix. Since there is an infinite number of PT parameters that could be produced by a human eye, it is unlikely that a human brain stores an infinite number of calibration matrices for each human eye. These observations inspire us to look for a simpler and computationally non-expensive solution which is to undertake three-dimensional (3D) projection in human-like binocular vision. In other words, it is an interesting question for us to answer, which is to say whether simpler and learning-friendly formulas for computing depth and displacement exist or not. If the answer is yes, these formulas must also be calibration-friendly (i.e., easy process on the fly or on the go). In this paper, we present an important discovery of a new solution to 3D projection in a human-like binocular vision system. This solution is computationally simpler and could be easily learned or calibrated on the fly. We know that the purpose of doing 3D projection in binocular vision is to undertake forward and inverse transformations (or mappings) between coordinates in 2D digital images and coordinates in a 3D analogue scene. The formulas underlying the new solution are accurate, easily computable, easily tunable (i.e., to be calibrated on the fly or on the go) and could be easily implemented by a neural system (i.e., a network of neurons or a network of computational flows). Experimental results have validated the newly derived formulas which are better than textbook solutions.