Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the robot.The presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller design.The task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the inner-loop.Data-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance parameters.In the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity measurement.On this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task space.The simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.

Zinc oxide nanoparticles inhibit osteosarcoma metastasis by downregulatingβ-catenin via HIF-1α/BNIP3/LC3B-mediated mitophagy pathway

Osteosarcoma(OS)therapy faces many challenges,especially the poor survival rate once metastasis occurs.Therefore,it is crucial to explore new OS treatment strategies that can efficiently inhibit OS metastasis.Bioactive nanoparticles such as zinc oxide nanoparticles(ZnO NPs)can efficiently inhibit OS growth,however,the effect and mechanisms of them on tumor metastasis are still not clear.In this study,we firstly prepared well-dispersed ZnO NPs and proved that ZnO NPs can inhibit OS metastasis-related malignant behaviors including migration,invasion,and epithelial-mesenchymal transition(EMT).RNA-Seqs found that differentially expressed genes(DEGs)in ZnO NP-treated OS cells were enriched in wingless/integrated(Wnt)and hypoxia-inducible factor-1(HIF-1)signaling pathway.We further proved that Zn^(2+)released from ZnO NPs induced downregulation ofβ-catenin expression via HIF-1α/BNIP3/LC3B-mediated mitophagy pathway.ZnO NPs combined with ICG-001,aβ-catenin inhibitor,showed a synergistic inhibitory effect on OS lung metastasis and a longer survival time.In addition,tissue microarray(TMA)of OS patients also detected much higherβ-catenin expression which indicated the role ofβ-catenin in OS development.In summary,our current study not only proved that ZnO NPs can inhibit OS metastasis by degradingβ-catenin in HIF-1α/BNIP3/LC3B-mediated mitophagy pathway,but also provided a far-reaching potential of ZnO NPs in clinical OS treatment with metastasis.

Parametric simulation of electron backscatter diffraction patterns through generative models

Recently,discriminative machine learning models have been widely used to predict various attributes from Electron Backscatter Diffraction(EBSD)patterns.However,there has never been any generative model developed for EBSD pattern simulation.On one hand,the training of generative models is much harder than that of discriminative ones;On the other hand,numerous variables affecting EBSD pattern formation make the input space high-dimensional and its relationship with the distribution of backscattered electrons complicated.In this study,we propose a framework(EBSD-CVAE/GAN)with great flexibility and scalability to realize parametric simulation of EBSD patterns.Compared with the frequently used forward model,EBSD-CVAE/GAN can take variables more than just orientation and generate corresponding EBSD patterns in a single run.The accuracy and quality of generated patterns are systematically evaluated.The model does not only summarize a distribution of backscattered electrons at a higher level,but also mitigates data scarcity in this field.