Generalization of Force Control Policies from Demonstrations for Constrained Robotic Motion Tasks

Koropouli, Vasiliki; Hirche, Sandra; Lee, Dongheui

doi:10.1007/s10846-015-0218-y

Cited by 11 publications

(7 citation statements)

References 32 publications

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“…However, their approach was limited, since it requires user interaction to generate new database entries. Similarly, in Koropouli et al ( 2015 ) a new policy was proposed where the input was motion data and the output was a force.…”

Section: Related Workmentioning

confidence: 99%

Phase-Synchronized Learning of Periodic Compliant Movement Primitives (P-CMPs)

Petrič

2020

Front. Neurorobot.

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Autonomous trajectory and torque profile synthesis through modulation and generalization require a database of motion with accompanying dynamics, which is typically difficult and time-consuming to obtain. Inspired by adaptive control strategies, this paper presents a novel method for learning and synthesizing Periodic Compliant Movement Primitives (P-CMPs). P-CMPs combine periodic trajectories encoded as Periodic Dynamic Movement Primitives (P-DMPs) with accompanying task-specific Periodic Torque Primitives (P-TPs). The state-of-the-art approach requires to learn TPs for each variation of the task, e.g., modulation of frequency. Comparatively, in this paper, we propose a novel P-TPs framework, which is both frequency and phase-dependent. Thereby, the executed P-CMPs can be easily modulated, and consequently, the learning rate can be improved. Moreover, both the kinematic and the dynamic profiles are parameterized, thus enabling the representation of skills using corresponding parameters. The proposed framework was evaluated on two robot systems, i.e., Kuka LWR-4 and Franka Emika Panda. The evaluation of the proposed approach on a Kuka LWR-4 robot performing a swinging motion and on Franka Emika Panda performing an exercise for elbow rehabilitation shows fast P-CTPs acquisition and accurate and compliant motion in real-world scenarios.

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Section: Related Workmentioning

confidence: 99%

Phase-Synchronized Learning of Periodic Compliant Movement Primitives (P-CMPs)

Petrič

2020

Front. Neurorobot.

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“…On the other hand, we previously proposed methods where arbitrary desired force-torque profiles could be tracked using iterative learning control [30,28]. Koropouli et al [31] have progressed beyond mere adaptation and employed generalization approaches for motion-based force control policies. By learning both the policy and the policy difference data using locally weighted regression (LWR), they could estimate the policy at new inputs through superposition of the training data.…”

Section: Generalization Of Contact Tasksmentioning

confidence: 99%

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

Kramberger

Gams

Nemec

et al. 2017

Robotics and Autonomous Systems

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A typical robot assembly operation involves contacts with the parts of the product to be assembled and consequently requires the knowledge of not only position and orientation trajectories but also the accompanying force-torque profiles for successful performance. To learn the execution of assembly operations even when the geometry of the product varies across task executions, the robot needs to be able to adapt its motion based on a parametric description of the current task condition, which is usually provided by geometrical properties of the parts involved in the assembly. In our previous work we showed how positional control policies can be generalized to different task conditions. In this paper we propose a complete methodology to generalize also the orientational trajectories and the accompanying force-torque profiles to compute the necessary control policy for a given condition of the assembly task. Our method is based on statistical generalization of successfully recorded executions at different task conditions, which are acquired by kinesthetic guiding. The parameters that describe the varying task conditions define queries into the recorded training data. To improve the execution of the skill after generalization, we combine the proposed approach with an adaptation method, thus enabling the refinement of the generalized assembly operation.

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“…Reinforcement learning constitutes a significant aspect of the artificial intelligence field with numerous applications ranging from medicine to robotics [ 25 , 26 ]. Researchers have recently focused on learning an appropriate modulation strategy by means of RL to adjust the impedance characteristic of robot [ 7 , 27 , 28 , 29 ]. Du [ 30 ] proposed a variable admittance control method based on fuzzy RL for human–robot interaction.…”

Section: Introductionmentioning

confidence: 99%

“…It is able to accurately model empirical data gathered in force field experiments. Koropouli [ 27 ] investigated the generalization problem of force control policy. The force-motion mapping policy was learned from a set of demonstrated data to endow robots with certain human-like adroitness.…”

Section: Introductionmentioning

confidence: 99%

Efficient Force Control Learning System for Industrial Robots Based on Variable Impedance Control

Zhang

Xia

et al. 2018

Sensors

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Learning variable impedance control is a powerful method to improve the performance of force control. However, current methods typically require too many interactions to achieve good performance. Data-inefficiency has limited these methods to learn force-sensitive tasks in real systems. In order to improve the sampling efficiency and decrease the required interactions during the learning process, this paper develops a data-efficient learning variable impedance control method that enables the industrial robots automatically learn to control the contact force in the unstructured environment. To this end, a Gaussian process model is learned as a faithful proxy of the system, which is then used to predict long-term state evolution for internal simulation, allowing for efficient strategy updates. The effects of model bias are reduced effectively by incorporating model uncertainty into long-term planning. Then the impedance profiles are regulated online according to the learned humanlike impedance strategy. In this way, the flexibility and adaptivity of the system could be enhanced. Both simulated and experimental tests have been performed on an industrial manipulator to verify the performance of the proposed method.

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Generalization of Force Control Policies from Demonstrations for Constrained Robotic Motion Tasks

Cited by 11 publications

References 32 publications

Phase-Synchronized Learning of Periodic Compliant Movement Primitives (P-CMPs)

Phase-Synchronized Learning of Periodic Compliant Movement Primitives (P-CMPs)

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

Efficient Force Control Learning System for Industrial Robots Based on Variable Impedance Control

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