Generalization of orientation trajectories and force-torque profiles for robotic assembly

Kramberger, Aljaž; Gams, Andrej; Nemec, Bojan; Chrysostomou, Dimitrios; Madsen, Ole; Ude, Aleš

doi:10.1016/j.robot.2017.09.019

Cited by 56 publications

(44 citation statements)

References 43 publications

(88 reference statements)

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“…As shown, each trajectory starts from the same initial position and ends at the same final position. The gripper is elevated to certain points depending on the height of the blocks, as visible from their values during [5][6][7][8][9][10] and [20][21][22][23][24][25] seconds. The gripper is brought close to the table at around second 15 to pick-up the toy.…”

Section: Handle Change In Cabinet Opening Task With Baxter Robotmentioning

confidence: 99%

Compliant Parametric Dynamic Movement Primitives

Uğur¹,

Girgin²

2019

Robotica

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SummaryIn this paper, we propose and implement an advanced manipulation framework that enables parametric learning of complex action trajectories along with their haptic feedback profiles. Our framework extends Dynamic Movement Primitives (DMPs) method with a new parametric nonlinear shaping function and a novel force-feedback coupling term. The nonlinear trajectories of the action control variables and the haptic feedback trajectories measured during execution are encoded with parametric temporal probabilistic models, namely parametric hidden Markov models (PHMMs). PHMMs enable autonomous segmentation of a taught skill based on the statistical information extracted from multiple demonstrations, and learning the relations between the model parameters and the properties extracted from the environment. Hidden states with high-variances in observation probabilities are interpreted as parts of the skill that could not be reliably learned and autonomously executed due to possibly uncertain or missing information about the environment. In those parts, our proposed force-feedback coupling term, which computes the deviation of the actual force feedback from the one predicted by the force-feedback PHMM, acts as a compliance term, enabling a human to scaffold the ongoing movement trajectory to accomplish the task. Our method is verified in a number of tasks including a real pick and place task that involves obstacles of different heights. Our robot, Baxter, successfully learned to generate the trajectory taking into the heights of the obstacles, move its end effector stiffly (and accurately) along the generated trajectory while passing through apertures, and allow human–robot collaboration in the autonomously detected segments of the motion, for example, when the gripper picks up the object whose position is not provided to the robot.

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Section: Handle Change In Cabinet Opening Task With Baxter Robotmentioning

confidence: 99%

Compliant Parametric Dynamic Movement Primitives

Uğur¹,

Girgin²

2019

Robotica

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“…Common methods for encoding LfD skills are Dynamic Movement Primitive (DMP) [22] and Hidden Semi-Markov Models [23] , where the trajectory is learned as a set of attractors. Recently, also force profile has been added to DMPs [24]. However, the problem of DMPs with force profile is the tight positional coupling between force and position, which in turn means that there must not be a lot of variance between demonstrations or initial contact locations in case of chamfers.…”

Section: Related Workmentioning

confidence: 99%

Learning from Demonstration for Hydraulic Manipulators

Suomalainen¹,

Koivumäki

Lampinen

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper presents, for the first time, a method for learning in-contact tasks from a teleoperated demonstration with a hydraulic manipulator. Due to the use of extremely powerful hydraulic manipulator, a force-reflected bilateral teleoperation is the most reasonable method of giving a human demonstration. An advanced subsystem-dynamic-based control design framework, virtual decomposition control (VDC), is used to design a stability-guaranteed controller for the teleoperation system, while taking into account the full nonlinear dynamics of the master and slave manipulators. The use of fragile force/ torque sensor at the tip of the hydraulic slave manipulator is avoided by estimating the contact forces from the manipulator actuators' chamber pressures. In the proposed learning method, it is observed that a surface-sliding tool has a friction-dependent range of directions (between the actual direction of motion and the contact force) from which the manipulator can apply force to produce the sliding motion. By this intuition, an intersection of these ranges can be taken over a motion to robustly find a desired direction for the motion from one or more demonstrations. The compliant axes required to reproduce the motion can be found by assuming that all motions outside the desired direction is caused by the environment, signalling the need for compliance. Finally, the learning method is incorporated to a novel VDC-based impedance control method to learn compliant behaviour from teleoperated human demonstrations. Experiments with 2-DOF hydraulic manipulator with a 475kg payload demonstrate the suitability and effectiveness of the proposed method to perform learning from demonstration (LfD) with heavy-duty hydraulic manipulators.

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“…However, the tight coupling of position and force/impedance profiles makes the methods susceptible to positional errors at initial contact or demonstrations of different lengths. In contrast, the method presented in this paper can naturally learn from demonstrations of different length, and for generalization does not require information such as hole depth as used in [14].…”

Section: Related Workmentioning

confidence: 99%

“…Since we wish to give preference to simpler models, for choosing the final D we take inspiration from Bayesian Information Criterion (BIC) [22], which is defined BIC = ln(n)k − 2 ln(L) (14) where n is the number of data points, k the number of parameters and L the likelihood of a model. Now we can choose the correct model on rows 12-14 on Algorithm 4.…”

Section: Learning Axes Of Compliancementioning

confidence: 99%

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Learning compliant assembly motions from demonstration

Suomalainen

Kyrki

2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We present a novel method for learning 6-D compliant motions from demonstration. The key advantage of our learning method compared to current Learning from Demonstration (LfD) methods is that we learn to take advantage of existing mechanical gradients (such as chamfers) with compliance to perform in-contact motions consisting of both translation and rotation, as in aligning workpieces or attaching hose couplers. We find the desired direction, which leads the robot's end-effector to the location shown in the demonstration either in free space or in contact, separately for translational and rotational motions. The key idea is to first compute a set of directions which would result in the observed motion at each timestep during a demonstration. By taking an intersection over all such sets from a demonstration we find a single desired direction which can reproduce the demonstrated motion. Finding the number of compliant axes and their directions in both rotation and translation is based on the assumption that in the presence of a desired direction of motion, all other observed motion is caused by the contact force of the environment, signalling the need for compliance. We evaluate the method on a KUKA LWR4+ robot with test setups imitating typical tasks where a human would use compliance to cope with positional uncertainty. Results show that the method can successfully learn and reproduce compliant motions by taking advantage of the geometry of the task, therefore reducing the need for initial accuracy. * This work was supported by Academy of Finland, decision 286580. † M. Suomalainen and V. Kyrki are with

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Generalization of orientation trajectories and force-torque profiles for robotic assembly

Cited by 56 publications

References 43 publications

Compliant Parametric Dynamic Movement Primitives

Compliant Parametric Dynamic Movement Primitives

Learning from Demonstration for Hydraulic Manipulators

Learning compliant assembly motions from demonstration

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