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: 🤖.

Statistical Learning for Humanoid Robots

Abstract: The complexity of the kinematic and dynamic structure of humanoid robots make conventional analytical approaches to control increasingly unsuitable for such systems. Learning techniques offer a possible way to aid controller design if insufficient analytical knowledge is available, and learning approaches seem mandatory when humanoid systems are supposed to become completely autonomous. While recent research in neural networks and statistical learning has focused mostly on learning from finite data sets without stringent constraints on computational efficiency, learning for humanoid robots requires a different setting, characterized by the need for real-time learning performance from an essentially infinite stream of incrementally arriving data. This paper demonstrates how even high-dimensional learning problems of this kind can successfully be dealt with by techniques from nonparametric regression and locally weighted learning. As an example, we describe the application of one of the most advanced of such algorithms, Locally Weighted Projection Regression (LWPR), to the on-line learning of three problems in humanoid motor control: the learning of inverse dynamics models for model-based control, the learning of inverse kinematics of redundant manipulators, and the learning of oculomotor reflexes. All these examples demonstrate fast, i.e., within seconds or minutes, learning convergence with highly accurate final peformance. We conclude that real-time learning for complex motor system like humanoid robots is possible with appropriately tailored algorithms, such that increasingly autonomous robots with massive learning abilities should be achievable in the near future.

The design and control of the flexible spine of a fully tendon-driven humanoid "Kenta"

Abstract: We are trying to realize a humanoid which has flexibility. If humanoids have a flexible structure, safety and posture variety can be achieved. We focus on the role of the human spine and muscle-driven system. By having a flexible spine, a humanoid will have safety and many degrees of freedom to realize a variety of postures. By driving joints by tension-controllable tendons, flexibility of the joints can be controlled. We developed a whole-body tendon-driven flexible-spine humanoid named "Kenta". This paper describes the design and control of Kenta, focusing on the design of the spine. The spine consists of ten joints, vertebrae and rubber disks, ribs, and forty muscles equipped with tension sensors. We also propose control methods of the spine. One uses a geometric virtual robot model and another is based on direct teaching. Using these methods, some whole-body motions are presented.

Balancing a humanoid robot using backdrive concerned torque control and direct angular momentum feedback

Abstract: A balance control method for a humanoid robot is presented. It consists of a contact torque controller which is designed to have a good backdrivability and a feedback control of the total angular momentum and the center of gravity of a robot. A simulation result of a balance control using a 26 DOF humanoid robot model is shown.