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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.

As tower cranes are highly dangerous, the problem of insufficient investment in tower safety needs to be solved urgently, and this study aims to solve the problem of insufficient investment in safety caused by the imbalance of interests of tower safety-related subjects and to propose targeted solutions.

Tower crane rental enterprises, contractors and government departments are selected to construct the game model, calculate the equilibrium point and stability and determine the optimal stabilization strategy. Finally, MATLAB software is used to model and simulate the impact of parameter changes on each party’s choice of strategies.

(1) The optimal combination of strategies is safety input by tower companies, leasing of qualified towers by contractors and providing non-financial incentives by the government. (2) The degree of synergistic coefficient γ, the level of government penalty coefficient α and the increase in accident probability p positively affect the adoption of proactive safety measures by tower crane leasing enterprises and contractors. (3) Excessive differences in safety costs may lead firms to adopt hostile safety measures.

This paper creatively uses safety input and tower crane leasing enterprises as the perspective and object of research on tower security. The research results are of great significance in guiding the government to formulate regulatory and incentive policies and in promoting enterprises to implement safety input to ensure construction safety collaboratively. It also provides new research cases for promoting the entire special equipment industry to realize adequate and effective safety input.