Learning human compliant behavior from demonstration for force-based robot manipulation

Deng, Zhen; Mi, Jinpeng; Chen, Zhixian; Einig, Lasse; Cheng, Zou; Zhang, Jianwei

doi:10.1109/robio.2016.7866342

Cited by 8 publications

(9 citation statements)

References 15 publications

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“…Many tasks where manipulation in contact is required also involve tools. They can either be rigidly attached to the robot arm or can be grasped by the robot for use; in this survey we do not differentiate between these cases, except by noting that grasping a tool always creates uncertainty regarding Wiping or polishing [18,19,20,21,22,23,24,25,26,27] Grinding or similar [28,29,30,31,32,33,34,35,36,37] Scooping [38,39] Peg-in-hole variants [40, 41, 42, 43, 44, 45, 17, 46, 47, 48, 49, 50] [51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64] [65, 66 Massage [95], velcro peeling [96] engraving [97] the location of the tooltip, which increases the need for compliance when in contact. There are of course methods to alleviate this uncertainty if enough information of the tool has been properly measured (for example, [98]).…”

Section: Tasks Requiring Manipulation In Contactmentioning

confidence: 99%

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A Survey of Robot Manipulation in Contact

Suomalainen¹,

Karayiannidis²,

Kyrki³

2021

Preprint

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Section: Tasks Requiring Manipulation In Contactmentioning

confidence: 99%

“…The simplest tasks in this region is perhaps wiping [18], which has been revisited upon multiple papers with different methods [19,20,21,22,23]; in this task the original material is not affected, since wiping mostly refers to cleaning the material. Another term used for, practically, the same task, is polishing [24,25,26,27].…”

Section: Environment Shapingmentioning

confidence: 99%

A Survey of Robot Manipulation in Contact

Suomalainen¹,

Karayiannidis²,

Kyrki³

2021

Preprint

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“…Constraint frames are usually chosen manually based on the task specifications Raibert and Craig (1981). Common choices include the tool frame (Kronander and Billard, 2014;Peternel et al, 2017) and the surface normals (Deng et al, 2016;Conkey and Hermans, 2019). The latter can also be estimated, e.g.…”

Section: Related Workmentioning

confidence: 99%

Quick Setup of Force-Controlled Industrial Gluing Tasks Using Learning From Demonstration

2021

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This paper presents a framework for programming in-contact tasks using learning by demonstration. The framework is demonstrated on an industrial gluing task, showing that a high quality robot behavior can be programmed using a single demonstration. A unified controller structure is proposed for the demonstration and execution of in-contact tasks that eases the transition from admittance controller for demonstration to parallel force/position control for the execution. The proposed controller is adapted according to the geometry of the task constraints, which is estimated online during the demonstration. In addition, the controller gains are adapted to the human behavior during demonstration to improve the quality of the demonstration. The considered gluing task requires the robot to alternate between free motion and in-contact motion; hence, an approach for minimizing contact forces during the switching between the two situations is presented. We evaluate our proposed system in a series of experiments, where we show that we are able to estimate the geometry of a curved surface, that our adaptive controller for demonstration allows users to achieve higher accuracy in a shorter demonstration duration when compared to an off-the-shelf controller for teaching implemented on a collaborative robot, and that our execution controller is able to reduce impact forces and apply a constant process force while adapting to the surface geometry.

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“…The literature in learning from demonstration for simultaneous control of position and force has focused on 1) learning which dimensions of the constraint frame should be selected for position or force control [6], [9], [11], [12] and, to a lesser extent, 2) learning the best constraint frame to control with respect to [7], [9]. A key insight that has motivated constraint selection methods is that dimensions of the constraint frame that consistently exhibit high variance over time in force and low variance over time in position should favor force control, and position control otherwise [9].…”

Section: A Learning Force/position Controlmentioning

confidence: 99%

“…A series of boolean checks in [6] over variance in force and position variables determines which axes of the robot's tool frame is enabled for PI force control or Cartesian impedance control. In [11], binary constraint selection for a hybrid force/position controller is made by enabling position control when the computed position variance is found to be greater than the force variance.…”

Section: A Learning Force/position Controlmentioning

confidence: 99%

Learning Task Constraints from Demonstration for Hybrid Force/Position Control

Conkey

Hermans

2019

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

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We present a novel method for learning hybrid force/position control from demonstration. We learn a dynamic constraint frame aligned to the direction of desired force using Cartesian Dynamic Movement Primitives. In contrast to approaches that utilize a fixed constraint frame, our approach easily accommodates tasks with rapidly changing task constraints over time. We activate only one degree of freedom for force control at any given time, ensuring motion is always possible orthogonal to the direction of desired force. Since we utilize demonstrated forces to learn the constraint frame, we are able to compensate for forces not detected by methods that learn only from the demonstrated kinematic motion, such as frictional forces between the end-effector and the contact surface. We additionally propose novel extensions to the Dynamic Movement Primitive (DMP) framework that encourage robust transition from free-space motion to in-contact motion in spite of environment uncertainty. We incorporate force feedback and a dynamically shifting goal to reduce forces applied to the environment and retain stable contact while enabling force control. Our methods exhibit low impact forces on contact and low steady-state tracking error.

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Learning human compliant behavior from demonstration for force-based robot manipulation

Cited by 8 publications

References 15 publications

A Survey of Robot Manipulation in Contact

A Survey of Robot Manipulation in Contact

Quick Setup of Force-Controlled Industrial Gluing Tasks Using Learning From Demonstration

Learning Task Constraints from Demonstration for Hybrid Force/Position Control

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