Humanoid robot

About: Humanoid robot is a research topic. Over the lifetime, 14387 publications have been published within this topic receiving 243674 citations. The topic is also known as: 🤖.

Neural-Dynamic-Method-Based Dual-Arm CMG Scheme With Time-Varying Constraints Applied to Humanoid Robots

Abstract: We propose a dual-arm cyclic-motion-generation (DACMG) scheme by a neural-dynamic method, which can remedy the joint-angle-drift phenomenon of a humanoid robot. In particular, according to a neural-dynamic design method, first, a cyclic-motion performance index is exploited and applied. This cyclic-motion performance index is then integrated into a quadratic programming (QP)-type scheme with time-varying constraints, called the time-varying-constrained DACMG (TVC-DACMG) scheme. The scheme includes the kinematic motion equations of two arms and the time-varying joint limits. The scheme can not only generate the cyclic motion of two arms for a humanoid robot but also control the arms to move to the desired position. In addition, the scheme considers the physical limit avoidance. To solve the QP problem, a recurrent neural network is presented and used to obtain the optimal solutions. Computer simulations and physical experiments demonstrate the effectiveness and the accuracy of such a TVC-DACMG scheme and the neural network solver.

Multicontact Locomotion of Legged Robots

Abstract: Locomotion of legged robots on arbitrary terrain using multiple contacts is yet an open problem. To tackle it, a common approach is to rely on reduced template models (e.g., the linear inverted pendulum). However, most of existing template models are based on some restrictive hypotheses that limit their range of applications. Moreover, reduced models are generally not able to cope with the constraints of the robot complete model, such as the kinematic limits. In this paper, we propose a complete solution relying on a generic template model, based on the centroidal dynamics, able to quickly compute multicontact locomotion trajectories for any legged robot on arbitrary terrains. The template model relies on exact dynamics and is thus not limited by arbitrary assumption. We also propose a generic procedure to handle feasibility constraints due to the robot's whole body as occupancy measures, and a systematic way to approximate them using offline learning in simulation. An efficient solver is finally obtained by introducing an original second-order approximation of the centroidal wrench cone. The effectiveness and the versatility of the approach are demonstrated in several multicontact scenarios with two humanoid robots both in reality and in simulation.

Virtual humanoid robot platform to develop controllers of real humanoid robots without porting

Abstract: This paper presents a virtual humanoid robot platform (V-HRP for short) on which we can develop the identical controller for a virtual humanoid robot and its real counterpart. The unification of the controllers for the virtual and real robot has been realized by introducing software adapters for two robots respectively and employing ART-Linux on which real-time processing is available at the user level. Thanks to the unification, the controllers can share softwares with the dynamics simulator of V-HRP, including the parameter parser, kinematics and dynamics computations and the collision detector. This feature can make the development of the controllers more efficient and the developed controllers more reliable.

Online trajectory generation for omnidirectional biped walking

Abstract: This paper describes the online generation of trajectories for omnidirectional walking on two legs. The gait can be parameterized using walking direction, walking speed, and rotational speed. Our approach has a low computational complexity and can be implemented on small onboard computers. We tested the proposed approach using our humanoid robot Jupp. The competitions in the RoboCup soccer domain showed that omnidirectional walking has advantages when acting in dynamic environments

Using robotic exploratory procedures to learn the meaning of haptic adjectives

Abstract: Delivering on the promise of real-world robotics will require robots that can communicate with humans through natural language by learning new words and concepts through their daily experiences. Our research strives to create a robot that can learn the meaning of haptic adjectives by directly touching objects. By equipping the PR2 humanoid robot with state-of-the-art biomimetic tactile sensors that measure temperature, pressure, and fingertip deformations, we created a platform uniquely capable of feeling the physical properties of everyday objects. The robot used five exploratory procedures to touch 51 objects that were annotated by human participants with 34 binary adjective labels. We present both static and dynamic learning methods to discover the meaning of these adjectives from the labeled objects, achieving average F1 scores of 0.57 and 0.79 on a set of eight previously unfelt items.