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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: 🤖.


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Journal Article•DOI•
TL;DR: It is shown how the competition drives humanoid robot research and serves as a benchmark to measure progress and how the league may evolve over the coming years until 2050, when a team of autonomous humanoid robots shall play soccer against the human world champion.
Abstract: This article describes the history and major achievements of the RoboCup Humanoid League from its start in 2002 to today. Furthermore, it gives an indication on how the league may evolve over the coming years until 2050, when a team of autonomous humanoid robots shall play soccer against the human world champion. We show how the competition drives humanoid robot research and serves as a benchmark to measure progress.

61 citations

Proceedings Article•DOI•
12 May 2009
TL;DR: This paper explains the methodology of performance evaluation of actuators and design concept of joint mechanism and Mathematical model of electro-hydrostatic transmission is presented.
Abstract: Robots must have similar mechanical impedance characteristics to humans in order to make safe and efficient contact. This impedance requirement applies not only to the surface but also to the actuation mechanisms. The objective of this research is to develop inherently flexible actuator by realizing backdrivability. A class of hydraulic actuation called electro-hydrostatic actuator was applied to knee joint in humanoid robots to satisfy flexibility and large torque output simultaneously. This paper explains the methodology of performance evaluation of actuators and design concept of joint mechanism. Mathematical model of electro-hydrostatic transmission is also presented. Evaluation of backdrivability, inertia modification control, and compliance control of developed mechanism are performed.

61 citations

Proceedings Article•DOI•
03 Dec 2010
TL;DR: In this paper, the authors present guidelines for the hand design of the Integrated Hand arm project of DLR for human-like manipulation in the context of humanoid robots, in particular when performing object manipulation.
Abstract: The impressive manipulation capabilities of the human hand are undoubtedly related to the thumb opposition. Such a versatility is highly desirable in the context of humanoid robots, in particular when performing object manipulation. Biomechanical data, surgery procedures and rehabilitation surveys represent an excellent base from which a robotic design can be inferred. This knowledge must be understood to identify the properties required for manipulation skills, and especially, to obtain a holistic view of the thumb functionality. Several designs have been realized, that concentrated on biomimetism or on classical mechanism designs. Therefore, it is currently difficult for designers to obtain a clear overview of the properties required for a functional robot thumb. In the present case, a robotic hand with size, forces, velocity and shape comparable to the human ones, is envisioned. Unlike most of robotic designs - where the fingers are modular and the thumb is simply a finger placed in opposition — the thumb benefits from an intensive functional analysis. This paper gathers anatomy, surgery and rehabilitation data and identifies the properties required for human like manipulation. Based on this synergy, guidelines are presented that are fused and applied to the hand design of the Integrated Hand arm project of DLR.

61 citations

Proceedings Article•DOI•
21 May 2018
TL;DR: In this article, the authors proposed two convex relaxations to the problem based on trust regions and soft constraints, which can compute time-optimized dynamically consistent trajectories sufficiently fast to make the approach realtime capable.
Abstract: Recently, the centroidal momentum dynamics has received substantial attention to plan dynamically consistent motions for robots with arms and legs in multi-contact scenarios. However, it is also non convex which renders any optimization approach difficult and timing is usually kept fixed in most trajectory optimization techniques to not introduce additional non convexities to the problem. But this can limit the versatility of the algorithms. In our previous work, we proposed a convex relaxation of the problem that allowed to efficiently compute momentum trajectories and contact forces. However, our approach could not minimize a desired angular momentum objective which seriously limited its applicability. Noticing that the non-convexity introduced by the time variables is of similar nature as the centroidal dynamics one, we propose two convex relaxations to the problem based on trust regions and soft constraints. The resulting approaches can compute time-optimized dynamically consistent trajectories sufficiently fast to make the approach realtime capable. The performance of the algorithm is demonstrated in several multi-contact scenarios for a humanoid robot. In particular, we show that the proposed convex relaxation of the original problem finds solutions that are consistent with the original non-convex problem and illustrate how timing optimization allows to find motion plans that would be difficult to plan with fixed timing ††Implementation details and demos can be found in the source code available at https://git-amd.tuebingen.mpg.de/bponton/timeoptimization.

61 citations

Proceedings Article•DOI•
16 May 2016
TL;DR: An automatic method for interactive control of physical humanoid robots based on high-level tasks that does not require manual specification of motion trajectories or specially-designed control policies is presented.
Abstract: We present an automatic method for interactive control of physical humanoid robots based on high-level tasks that does not require manual specification of motion trajectories or specially-designed control policies. The method is based on the combination of a model-based policy that is trained off-line in simulation and sends high-level commands to a model-free controller that executes these commands on the physical robot. This low-level controller simultaneously learns and adapts a local model of dynamics on-line and computes optimal controls under the learned model. The high-level policy is trained using a combination of trajectory optimization and neural network learning, while considering physical limitations such as limited sensors and communication delays. The entire system runs in real-time on the robot's computer and uses only on-board sensors. We demonstrate successful policy execution on a range of tasks such as leaning, hand reaching, and robust balancing behaviors atop a tilting base on the physical robot and in simulation.

60 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023253
2022759
2021573
2020647
2019801
2018921