ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots

AbstractIn this paper, we propose an effective planning method for whole-body motions of humanoid robots under various conditions for achieving the task. In motion planning, various constraints such as range of motion have to be considered. Specifically, it is important to maintain balance in whole-body motion. In order to be useful in an unpredictable environment, rapid planning is an essential problem. In this research, via-point representation is used for assigning sufficient conditions to deal with various constraints in the movement. The position, posture and velocity of the robot are constrained as a state of a via-point. In our algorithm, the feasible motions are planned by modifying via-points. Furthermore, we formulate the motion planning problem as a simple iterative method with a Linear Programming (LP) problem for efficiency of the motion planning. We have applied the method to generate the kicking motion of a HOAP-3 humanoid robot. We confirmed that the robot can successfully score a goal with various courses corresponding to changing conditions of the location of an obstacle. The computation time was less than two seconds. These results indicate that the proposed algorithm can achieve efficient motion planning.

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Real-Time Whole-Body Imitation by Humanoid Robots and Task-Oriented Teleoperation Using an Analytical Mapping Method and Quantitative Evaluation

Applied Sciences ◽

10.3390/app8102005 ◽

2018 ◽

Vol 8 (10) ◽

pp. 2005 ◽

Cited By ~ 1

Author(s):

Zhijun Zhang ◽

Yaru Niu ◽

Ziyi Yan ◽

Shuyang Lin

Keyword(s):

Real Time ◽

Humanoid Robots ◽

Mapping Method ◽

Whole Body ◽

Mode Switching ◽

Natural Mode ◽

And Performance ◽

Virtual Joints ◽

Task Oriented ◽

And Task

Due to the limitations on the capabilities of current robots regarding task learning and performance, imitation is an efficient social learning approach that endows a robot with the ability to transmit and reproduce human postures, actions, behaviors, etc., as a human does. Stable whole-body imitation and task-oriented teleoperation via imitation are challenging issues. In this paper, a novel comprehensive and unrestricted real-time whole-body imitation system for humanoid robots is designed and developed. To map human motions to a robot, an analytical method called geometrical analysis based on link vectors and virtual joints (GA-LVVJ) is proposed. In addition, a real-time locomotion method is employed to realize a natural mode of operation. To achieve safe mode switching, a filter strategy is proposed. Then, two quantitative vector-set-based methods of similarity evaluation focusing on the whole body and local links, called the Whole-Body-Focused (WBF) method and the Local-Link-Focused (LLF) method, respectively, are proposed and compared. Two experiments conducted to verify the effectiveness of the proposed methods and system are reported. Specifically, the first experiment validates the good stability and similarity features of our system, and the second experiment verifies the effectiveness with which complicated tasks can be executed. At last, an imitation learning mechanism in which the joint angles of demonstrators are mapped by GA-LVVJ is presented and developed to extend the proposed system.

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Motion separating based whole body motion planning for humanoid robots using a gradient descent method

2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics ◽

10.1109/aim.2008.4601805 ◽

2008 ◽

Author(s):

Hwan-Joo Kwak ◽

Gwi-Tae Park

Keyword(s):

Motion Planning ◽

Gradient Descent ◽

Humanoid Robots ◽

Descent Method ◽

Body Motion ◽

Whole Body ◽

Gradient Descent Method

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Sensor-based Whole-Body Planning/Replanning for Humanoid Robots

2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids) ◽

10.1109/humanoids43949.2019.9035017 ◽

2019 ◽

Author(s):

Paolo Ferrari ◽

Marco Cognetti ◽

Giuseppe Oriolo

Keyword(s):

Humanoid Robots ◽

Whole Body

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Whole-body motion and footstep planning for humanoid robots with multi-heuristic search

Robotics and Autonomous Systems ◽

10.1016/j.robot.2019.03.007 ◽

2019 ◽

Vol 116 ◽

pp. 51-63 ◽

Cited By ~ 1

Author(s):

Rizwan Asif ◽

Ali Athar ◽

Faisal Mehmood ◽

Fahad Islam ◽

Yasar Ayaz

Keyword(s):

Heuristic Search ◽

Humanoid Robots ◽

Body Motion ◽

Whole Body

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Cooperative Vision-Based Object Transportation by Two Humanoid Robots in a Cluttered Environment

International Journal of Humanoid Robotics ◽

10.1142/s0219843617500189 ◽

2017 ◽

Vol 14 (03) ◽

pp. 1750018 ◽

Cited By ~ 4

Author(s):

Antoine Rioux ◽

Claudia Esteves ◽

Jean-Bernard Hayet ◽

Wael Suleiman

Keyword(s):

Real Life ◽

Humanoid Robots ◽

Whole Body ◽

Large Object ◽

Cluttered Environments ◽

Cluttered Environment ◽

Planning Algorithm ◽

Feedback Error ◽

Object Transportation ◽

Incremental Construction

Although in recent years, there have been quite a few studies aimed at the navigation of robots in cluttered environments, few of these have addressed the problem of robots navigating while moving a large or heavy object. Such a functionality is especially useful when transporting objects of different shapes and weights without having to modify the robot hardware. In this work, we tackle the problem of making two humanoid robots navigate in a cluttered environment while transporting a very large object that simply could not be moved by a single robot. We present a complete navigation scheme, from the incremental construction of a map of the environment and the computation of collision-free trajectories to the design of the control to execute those trajectories. We present experiments made on real NAO robots, equipped with RGB-D sensors mounted on their heads, moving an object around obstacles. Our experiments show that a significantly large object can be transported without modifying the robot main hardware, and therefore that our scheme enhances the humanoid robots capacities in real-life situations. Our contributions are: (1) a low-dimension multi-robot motion planning algorithm that finds an obstacle-free trajectory, by using the constructed map of the environment as an input, (2) a framework that produces continuous and consistent odometry data, by fusing the visual and the robot odometry information, (3) a synchronization system that uses the projection of the robots based on their hands positions coupled with the visual feedback error computed from a frontal camera, (4) an efficient real-time whole-body control scheme that controls the motions of the closed-loop robot–object–robot system.

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