Prioritized Inverse Kinematics with Multiple Task Definitions

An, Sang-ik; Lee, Dongheui

doi:10.1109/icra.2015.7139376

Cited by 25 publications

(8 citation statements)

References 21 publications

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“…External forces arising from kinesthetic teaching are converted into velocity commands with a standard admittance control. This velocity is added to a stack of tasks with variable priority order and executed using the prioritized inverse kinematics approach by An and Lee ( 2015 ). The variable priority order is needed to select the task to execute in case of conflicts, for example when the user tries to distract the end-effector from the nominal trajectory.…”

Section: Variable Impedance Learning Control (Vilc)mentioning

confidence: 99%

Variable Impedance Control and Learning—A Review

2020

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Robots that physically interact with their surroundings, in order to accomplish some tasks or assist humans in their activities, require to exploit contact forces in a safe and proficient manner. Impedance control is considered as a prominent approach in robotics to avoid large impact forces while operating in unstructured environments. In such environments, the conditions under which the interaction occurs may significantly vary during the task execution. This demands robots to be endowed with online adaptation capabilities to cope with sudden and unexpected changes in the environment. In this context, variable impedance control arises as a powerful tool to modulate the robot's behavior in response to variations in its surroundings. In this survey, we present the state-of-the-art of approaches devoted to variable impedance control from control and learning perspectives (separately and jointly). Moreover, we propose a new taxonomy for mechanical impedance based on variability, learning, and control. The objective of this survey is to put together the concepts and efforts that have been done so far in this field, and to describe advantages and disadvantages of each approach. The survey concludes with open issues in the field and an envisioned framework that may potentially solve them.

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Section: Variable Impedance Learning Control (Vilc)mentioning

confidence: 99%

Variable Impedance Control and Learning—A Review

2020

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“…Multiple tasks, such as end-effector and null-space motion primitives, can be taught physically one by one using the compliance controller. In [2], the taught tasks and the user's physical guidance are treated as prioritized tasks and their priorities are adjusted dynamically according to the exerted human force by task transition control.…”

Section: Kinesthetic Teachingmentioning

confidence: 99%

“…[67] is another example, where a weighted sum of torques, each minimizing a different cost function, provides a convenient representation but requires the mixing coefficients to be manually selected. In [2], a description of multiple task definitions is constructed with the concept of flexible priority structures. It handles efficiently unprioritized or prioritized accumulations of tasks and priority switches by incorporating interpolation in joint space.…”

Section: Task Priority Learningmentioning

confidence: 99%

Learning Control

Calinon

Lee

2018

Humanoid Robotics: A Reference

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This chapter presents an overview of learning approaches for the acquisition of controllers and movement skills in humanoid robots. The term learning control refers to the process of acquiring a control strategy to achieve a task. While the definition is in some cases restrained to trialand-error learning, we present here learning control in a broader perspective, with a focus on the representation of skills to be acquired, and on the different learning strategies that can contribute to the acquisition of robust and adaptive controllers for humanoids.

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“…This approach guarantees the correct end-effector task execution (i.e., convergence to the target and collision avoidance) and, in many situations, it prevents collisions with the robot body. If collision avoidance is the most important task for a certain application, task priorities can be changed to guarantee that the collision avoidance has always priority (Falco and Natale, 2014;An and Lee, 2015).…”

Section: Modulation Considering the Robot Bodymentioning

confidence: 99%

Human-aware motion reshaping using dynamical systems

Saveriano

Hirt

Lee

2017

Pattern Recognition Letters

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In this work, we present a real-time approach for human-aware motion replanning using a two-level hierarchical architecture. The lower level leverages stable dynamical systems to generate motor commands and to online reshape the robot trajectories. The reshaping strategy modifies the velocity of the robot to match three requirements: i) to increase the human safety in case of close interaction with the robot, ii) to guarantee the correct task execution in case of unforeseen obstacles (including the human), and iii) to replan online the current task taking into account the human behavior. The lower level has to execute all needed computations in real-time. To this end, we also propose a novel approach that leverages depth space structure and parallel programming to rapidly and accurately estimate the robot-obstacle distances. The higher level of the architecture monitors the human and provides to the lower level information about the human status. The proposed approach has been tested in a human robot interaction scenario, showing promising results in terms of safe human-robot coexistence.

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Prioritized Inverse Kinematics with Multiple Task Definitions

Cited by 25 publications

References 21 publications

Variable Impedance Control and Learning—A Review

Variable Impedance Control and Learning—A Review

Learning Control

Human-aware motion reshaping using dynamical systems

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