ORB-SLAM based humanoid robot location and navigation system

Aiming at the indoor location and navigation problem of humanoid biped robot with complex motion structure, a humanoid biped robot localization and navigation system based on ORB-SLAM is designed. Firstly, the working principle of ORB-SLAM is analyzed, and it is improved to realize the function of missing map reading and generating dense point cloud map. Secondly, the dense point cloud map is converted to octomap, and then the conversion of 3D map to 2D map is completed. The SBPL planning library is improved to carry out the path planning of the robot, and the path planning based on the boundary exploration is realized. Finally, the experimental verification is carried out on the biped robot to verify the effectiveness of the location and navigation system design in the indoor environment

Robot Motion Planning Method Based on Incremental High-Dimensional Mixture Probabilistic Model

The sampling-based motion planner is the mainstream method to solve the motion planning problem in high-dimensional space. In the process of exploring robot configuration space, this type of algorithm needs to perform collision query on a large number of samples, which greatly limits their planning efficiency. Therefore, this paper uses machine learning methods to establish a probabilistic model of the obstacle region in configuration space by learning a large number of labeled samples. Based on this, the high-dimensional samples' rapid collision query is realized. The influence of number of Gaussian components on the fitting accuracy is analyzed in detail, and a self-adaptive model training method based on Greedy expectation-maximization (EM) algorithm is proposed. At the same time, this method has the capability of online updating and can eliminate model fitting errors due to environmental changes. Finally, the model is combined with a variety of sampling-based motion planners and is validated in multiple sets of simulations and real world experiments. The results show that, compared with traditional methods, the proposed method has significantly improved the planning efficiency

American Sign Language Translation Using Wearable Inertial and Electromyography Sensors for Tracking Hand Movements and Facial Expressions

A sign language translation system can break the communication barrier between hearing-impaired people and others. In this paper, a novel American sign language (ASL) translation method based on wearable sensors was proposed. We leveraged inertial sensors to capture signs and surface electromyography (EMG) sensors to detect facial expressions. We applied a convolutional neural network (CNN) to extract features from input signals. Then, long short-term memory (LSTM) and transformer models were exploited to achieve end-to-end translation from input signals to text sentences. We evaluated two models on 40 ASL sentences strictly following the rules of grammar. Word error rate (WER) and sentence error rate (SER) are utilized as the evaluation standard. The LSTM model can translate sentences in the testing dataset with a 7.74% WER and 9.17% SER. The transformer model performs much better by achieving a 4.22% WER and 4.72% SER. The encouraging results indicate that both models are suitable for sign language translation with high accuracy. With complete motion capture sensors and facial expression recognition methods, the sign language translation system has the potential to recognize more sentences

Learning adaptive reaching and pushing skills using contact information

In this paper, we propose a deep reinforcement learning-based framework that enables adaptive and continuous control of a robot to push unseen objects from random positions to the target position. Our approach takes into account contact information in the design of the reward function, resulting in improved success rates, generalization for unseen objects, and task efficiency compared to policies that do not consider contact information. Through reinforcement learning using only one object in simulation, we obtain a learned policy for manipulating a single object, which demonstrates good generalization when applied to the task of pushing unseen objects. Finally, we validate the effectiveness of our approach in real-world scenarios

Co-inhibition of HDAC and MLL-menin interaction targets MLL-rearranged acute myeloid leukemia cells via disruption of DNA damage checkpoint and DNA repair.

While the aberrant translocation of the mixed-lineage leukemia (MLL) gene drives pathogenesis of acute myeloid leukemia (AML), it represents an independent predictor for poor prognosis of adult AML patients. Thus, small molecule inhibitors targeting menin-MLL fusion protein interaction have been emerging for the treatment of MLL-rearranged AML. As both inhibitors of histone deacetylase (HDAC) and menin-MLL interaction target the transcription-regulatory machinery involving epigenetic regulation of chromatin remodeling that governs the expression of genes involved in tumorigenesis, we hypothesized that these two classes of agents might interact to kill MLL-rearranged (MLL-r) AML cells. Here, we report that the combination treatment with subtoxic doses of the HDAC inhibitor chidamide and the menin-MLL interaction inhibitor MI-3 displayed a highly synergistic anti-tumor activity against human MLL-r AML cells in vitro and in vivo, but not those without this genetic aberration. Mechanistically, co-exposure to chidamide and MI-3 led to robust apoptosis in MLL-r AML cells, in association with loss of mitochondrial membrane potential and a sharp increase in ROS generation. Combined treatment also disrupted DNA damage checkpoint at the level of CHK1 and CHK2 kinases, rather than their upstream kinases (ATR and ATM), as well as DNA repair likely via homologous recombination (HR), but not non-homologous end joining (NHEJ). Genome-wide RNAseq revealed gene expression alterations involving several potential signaling pathways (e.g., cell cycle, DNA repair, MAPK, NF-κB) that might account for or contribute to the mechanisms of action underlying anti-leukemia activity of chidamide and MI-3 as a single agent and particularly in combination in MLL-r AML. Collectively, these findings provide a preclinical basis for further clinical investigation of this novel targeted strategy combining HDAC and Menin-MLL interaction inhibitors to improve therapeutic outcomes in a subset of patients with poor-prognostic MLL-r leukemia

Quantum Neuronal Sensing of Quantum Many-Body States on a 61-Qubit Programmable Superconducting Processor

Classifying many-body quantum states with distinct properties and phases of matter is one of the most fundamental tasks in quantum many-body physics. However, due to the exponential complexity that emerges from the enormous numbers of interacting particles, classifying large-scale quantum states has been extremely challenging for classical approaches. Here, we propose a new approach called quantum neuronal sensing. Utilizing a 61 qubit superconducting quantum processor, we show that our scheme can efficiently classify two different types of many-body phenomena: namely the ergodic and localized phases of matter. Our quantum neuronal sensing process allows us to extract the necessary information coming from the statistical characteristics of the eigenspectrum to distinguish these phases of matter by measuring only one qubit. Our work demonstrates the feasibility and scalability of quantum neuronal sensing for near-term quantum processors and opens new avenues for exploring quantum many-body phenomena in larger-scale systems.Comment: 7 pages, 3 figures in the main text, and 13 pages, 13 figures, and 1 table in supplementary material