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: 🤖.

Emotion and sociable humanoid robots

Abstract: This paper focuses on the role of emotion and expressive behavior in regulating social interaction between humans and expressive anthropomorphic robots, either in communicative or teaching scenarios. We present the scientific basis underlying our humanoid robot's emotion models and expressive behavior, and then show how these scientific viewpoints have been adapted to the current implementation. Our robot is also able to recognize affective intent through tone of voice, the implementation of which is inspired by the scientific findings of the developmental psycholinguistics community. We first evaluate the robot's expressive displays in isolation. Next, we evaluate the robot's overall emotive behavior (i.e. the coordination of the affective recognition system, the emotion and motivation systems, and the expression system) as it socially engages nave human subjects face-to-face.

On Learning, Representing, and Generalizing a Task in a Humanoid Robot

Abstract: We present a programming-by-demonstration framework for generically extracting the relevant features of a given task and for addressing the problem of generalizing the acquired knowledge to different contexts. We validate the architecture through a series of experiments, in which a human demonstrator teaches a humanoid robot simple manipulatory tasks. A probability-based estimation of the relevance is suggested by first projecting the motion data onto a generic latent space using principal component analysis. The resulting signals are encoded using a mixture of Gaussian/Bernoulli distributions (Gaussian mixture model/Bernoulli mixture model). This provides a measure of the spatio-temporal correlations across the different modalities collected from the robot, which can be used to determine a metric of the imitation performance. The trajectories are then generalized using Gaussian mixture regression. Finally, we analytically compute the trajectory which optimizes the imitation metric and use this to generalize the skill to different contexts

Capture Point: A Step toward Humanoid Push Recovery

Abstract: It is known that for a large magnitude push a human or a humanoid robot must take a step to avoid a fall. Despite some scattered results, a principled approach towards "when and where to take a step" has not yet emerged. Towards this goal, we present methods for computing capture points and the capture region, the region on the ground where a humanoid must step to in order to come to a complete stop. The intersection between the capture region and the base of support determines which strategy the robot should adopt to successfully stop in a given situation. Computing the capture region for a humanoid, in general, is very difficult. However, with simple models of walking, computation of the capture region is simplified. We extend the well-known linear inverted pendulum model to include a flywheel body and show how to compute exact solutions of the capture region for this model. Adding rotational inertia enables the humanoid to control its centroidal angular momentum, much like the way human beings do, significantly enlarging the capture region. We present simulations of a simple planar biped that can recover balance after a push by stepping to the capture region and using internal angular momentum. Ongoing work involves applying the solution from the simple model as an approximate solution to more complex simulations of bipedal walking, including a 3D biped with distributed mass.

The 3D linear inverted pendulum mode: a simple modeling for a biped walking pattern generation

Abstract: For 3D walking control of a biped robot we analyze the dynamics of a 3D inverted pendulum in which motion is constrained to move along an arbitrarily defined plane. This analysis yields a simple linear dynamics, the 3D linear inverted pendulum mode (3D-LIPM). Geometric nature of trajectories under the 3D-LIPM and a method for walking pattern generation are discussed. A simulation result of a walking control using a 12-DOF biped robot model is also shown.