Lifelike Agility and Play on Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models

Summarizing knowledge from animals and human beings inspires robotic innovations. In this work, we propose a framework for driving legged robots act like real animals with lifelike agility and strategy in complex environments. Inspired by large pre-trained models witnessed with impressive performance in language and image understanding, we introduce the power of advanced deep generative models to produce motor control signals stimulating legged robots to act like real animals. Unlike conventional controllers and end-to-end RL methods that are task-specific, we propose to pre-train generative models over animal motion datasets to preserve expressive knowledge of animal behavior. The pre-trained model holds sufficient primitive-level knowledge yet is environment-agnostic. It is then reused for a successive stage of learning to align with the environments by traversing a number of challenging obstacles that are rarely considered in previous approaches, including creeping through narrow spaces, jumping over hurdles, freerunning over scattered blocks, etc. Finally, a task-specific controller is trained to solve complex downstream tasks by reusing the knowledge from previous stages. Enriching the knowledge regarding each stage does not affect the usage of other levels of knowledge. This flexible framework offers the possibility of continual knowledge accumulation at different levels. We successfully apply the trained multi-level controllers to the MAX robot, a quadrupedal robot developed in-house, to mimic animals, traverse complex obstacles, and play in a designed challenging multi-agent Chase Tag Game, where lifelike agility and strategy emerge on the robots. The present research pushes the frontier of robot control with new insights on reusing multi-level pre-trained knowledge and solving highly complex downstream tasks in the real world

A bio-inspired methodology of automatic perching for unmanned aerial vehicles

Current unmanned aerial vehicles (UAVs) have to stay airborne during surveillance missions, decreasing their energy efficiency dramatically and therefore limiting their endurance significantly. On the other hand, birds perch to conserve energy while maintaining surveillance over their domain. A biomimetic methodology of automatic perching for UAVs is thus a promising solution to their endurance problem. Firstly, an experimental study of parrots’ perching is conducted to obtain bio-inspirations of perching principles, and the perching procedure is generalized into three stages following which the perching methodology is addressed. Secondly, varying tau-dot, as observed from the approaching flight of parrots, is proposed with a fuzzy logic for perching flight guidance of UAVs and it outperforms the conventional assumption of constant tau-dot in terms of flight time. Thirdly, a scale-dependent expansion model (SEM) is derived for visual perception of tau-dot, and three visual identification algorithms are evaluated for best perception performance. Experiment results verify the effectiveness of the SEM, making onboard autonomous guidance possible. However, improvement on perception accuracy and reliability is still needed. Fourthly, a two-dimensional perching model of quadrotors covering dynamic interaction with perch is established based on analysis of the balancing procedure of parrots after touchdown. Simulation validates the applicability of the model to biomimetic perching. Finally, a gripping perching mechanism featuring force amplification and sensing is designed, and a fuzzy control law is proposed for automatic gripping. Experiments of indoor and outdoor remotely controlled perching, indoor automatic perching, and dynamic automatic gripping are performed, and results show that the perching mechanism is capable of fulfilling reliable and automatic attachment to perch. The proposed methodology of automatic perching for UAVs covers the complete perching procedure, although its effectiveness has only been verified for each perching stage individually. Future work on enhancement of each component of the methodology and their integration can be done to validate overall effectiveness.DOCTOR OF PHILOSOPHY (MAE

Downregulation of circ-YES1 suppresses NSCLC migration and proliferation through the miR-142-3p–HMGB1 axis

Abstract Background Circular RNAs (circRNAs) are a new family of abundant regulatory RNAs with roles in various types of cancer. While the hsa_circ_0046701 (circ-YES1) function in non-small cell lung cancer (NSCLC) is unclear. Methods Circ-YES1 expression in normal pulmonary epithelial and NSCLC cells was examined. The small interfering RNA for circ-YES1 was prepared, cell proliferation and migration were assessed. Tumorigenesis in nude mice was assayed to validate the role of circ-YES1. Bioinformatics analyses and luciferase reporter assays were utilized to identify downstream targets of circ-YES1. Results Compared to normal pulmonary epithelial cells, the circ-YES1 expression increased in NSCLC cells, and cell proliferation and migration were suppressed after circ-YES1 knockdown. Both high mobility group protein B1 (HMGB1) and miR-142-3p were found to be downstream targets of circ-YES1, and miR-142-3p inhibition and HMGB1 overexpression reversed the effects of circ-YES1 knockdown on cell proliferation and migration. Similarly, HMGB1 overexpression reversed the miR-142-3p overexpression effects on these two processes. The imaging experiment results revealed that circ-YES1 knockdown impeded tumor development and metastasis in a nude mouse xenograft model. Conclusion Taken together, our results show that circ-YES1 promotes tumor development through the miR-142-3p–HMGB1 axis and support the development of circ-YES1 probability as a new therapeutic NSCLC target