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Human beings are able to adjust their arm stiffness in daily life tasks. This paper aims to enable a robot to learn these human-like variable stiffness motor skills autonomously.

The paper presents a reinforcement learning method to enable a robot to learn variable stiffness motor skills autonomously. Firstly, the variable stiffness motor skills are encoded by the previously proposed dynamical movement primitives and stiffness primitives (DMP-SP) framework, which is able to generate both motion and stiffness curves for robots. The admittance controller is then used to make a robot follow the motion and stiffness curves. The authors then use the policy improvement with path integrals (PI2) algorithm to optimize the robot motion and stiffness curves iteratively.

The performance of the proposed method is evaluated on an UR10 robot by two different tasks: a) via-point task, b) sweeping the floor. The results show that after training, the robot is capable of accomplishing the tasks safely and compliantly.

The method can help the robots walk out of the isolated environment and accelerate their integration into human being’s daily life.

This paper uses reinforcement learning method to improve DMP-SP framework, thus allowing the robots to learn variable stiffness motor skills autonomously with no need for extra sensors.

Research on decision rights partitioning and its impact on platform performance has predominantly focused on single rights, leading to inconclusive results. This study is driven by a more nuanced objective of exploring diverse governance models that can enhance the performance of sharing platforms across different contexts. Rather than delegating single decision right to users, this approach partitions several essential decision rights concurrently throughout the transaction process. By examining the complex relationships between multiple decision rights partitioning and platform performance, this study identifies and explains suitable governance models that are tailored to specific contextual factors for improving the performance of sharing platforms.

Collecting data from 60 sharing platforms in China, this study employs a combination of cluster and configuration analyses to address research questions.

The study explores three strategic decision rights partitioning modes widely adopted by sharing platforms. It further identifies four governance models for sharing platforms, which are termed as conservative seller model, conservative buyer model, aggressive seller model and aggressive buyer model, related to certain contextual factors.

In addressing platform governance as key to sharing platform success, the study contributes to the literature by investigating how multiple-rights partitioning portfolios and strategic differentiation in decision rights partitioning can enhance platform performance.