Grasping is a basic but challenging task in intelligent robotic manipulation, and grasping pose detection is the key in this task. Most current work is shifting from 2D planar grasping to more flexible six-degree-of-freedom (6-DoF) grasping, and some significant progress has been made. However, there are still limitations such as low success rate and poor robustness in cluttered scenes. In this paper, we investigate 6-DoF grasping in cluttered scenes, and propose a cascaded multitarget learning network based on self-attention mechanism and multiscale sampling. The self-attention mechanism effectively improves the network’s attention to the correct grasping pose, while multiscale sampling improves the network’s adaptability to grasping objects of different sizes. In addition, we propose a dual-objective evaluation metric based on the force-closure metric and the center-of-mass distance metric, which can make a more reasonable and reliable evaluation of the grasping poses in the dataset. Our model is evaluated on large-scale benchmarks as well as the real robot system. The proposed method achieves state-of-the-art results on GraspNet-1Billion (8.8+AP), and shows 95.24% success rates in real cluttered scenes.

This paper investigates the tracking control problem of a class of high-order distributed systems subjected to limited communication bandwidth. An event-triggered control method is proposed, where the controller is triggered only when specific events happen. Moreover, the computational complexity is reduced by introducing command filters for virtual control signals. Specifically, the backstepping scheme is adopted as the main design framework, by which the n-th order nonlinear system is divided into n command-cascaded first order subsystems. And virtual control commands are sent through a second-order low-pass filter, by which the time derivatives of the virtual commands can be obtained directly. The theoretical analysis shows the stability of the proposed method. The tracking performance is illustrated by a simulation example.

In surgical simulation, the Navier-Stokes (N-S) equation is commonly employed to imitate the physical characteristics of bleeding and the smooth particle hydrodynamics (SPHs) algorithm is applied to solve the numerical solution of the N-S equation. However, blood is viscous, incompressible and non-Newtonian fluid whose physical properties cannot be fully incorporated by the simple N-S equation, and the kernel approximation of the SPH algorithm may lead to both edge and volume distortions plus high computational cost. In this paper, both the tension force and the effect of platelets on the viscous force of bleeding particles are incorporated into the N-S equation in order to render more realistic visual effect and biological features of bleeding in surgical simulation. Constant core radius of the kernel function of the SPH algorithm is substituted with a function of particle density, avoiding potential edge distortions in simulating bleeding area. A repulsive force between particles is introduced, which effectively prevents volume distortions. Besides, accelerated search for particles based on the cube mesh improves the computational efficiency. The simulation results show that the presented simulation method leads to smooth bleeding surface and improves the visual effects of edge and volume in comparison with existing methods, and relatively high computational efficiency can be achieved as well.

MOTIVATION: The mutations of cancers can encode the seeds of their own destruction, in the form of T-cell recognizable immunogenic peptides, also known as neoantigens. It is computationally challenging, however, to accurately prioritize the potential neoantigen candidates according to their ability of activating the T-cell immunoresponse, especially when the somatic mutations are abundant. Although a few neoantigen prioritization methods have been proposed to address this issue, advanced machine learning model that is specifically designed to tackle this problem is still lacking. Moreover, none of the existing methods considers the original DNA loci of the neoantigens in the perspective of 3D genome which may provide key information for inferring neoantigens' immunogenicity. RESULTS: In this study, we discovered that DNA loci of the immunopositive and immunonegative MHC-I neoantigens have distinct spatial distribution patterns across the genome. We therefore used the 3D genome information along with an ensemble pMHC-I coding strategy, and developed a group feature selection-based deep sparse neural network model (DNN-GFS) that is optimized for neoantigen prioritization. DNN-GFS demonstrated increased neoantigen prioritization power comparing to existing sequence-based approaches. We also developed a webserver named deepAntigen (http://yishi.sjtu.edu.cn/deepAntigen) that implements the DNN-GFS as well as other machine learning methods. We believe that this work provides a new perspective toward more accurate neoantigen prediction which eventually contribute to personalized cancer immunotherapy. AVAILABILITY AND IMPLEMENTATION: Data and implementation are available on webserver: http://yishi.sjtu.edu.cn/deepAntigen. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

With the development of science and requirement, Integration of Communication and Radar with properties of more functions, lower buck and safety is becoming a trend and draws more attention of researchers. Current researches are focusing on communication or radar respectively or just focusing on signal generation. This paper proposes a solution of integrated system for SAR radar communication based on OFDM techniques in high speed scenario. The receiver can reconstruct the reference signal which is adopted for radar pulse compression procedure by communication system directly. In high speed scenario, channel response changes fast, comb type pilots and channel estimation are added to equalize the channel response to reduce bit-error-rate in communication system. Finally, the simulation are used to validate the performance of the proposed algorithm.

In this paper, two gesture design methods for the humanoid robot NAO are proposed and compared. The first method is mapping human motions to the robot by an RGB-D sensor and kinematic modeling. The second method is based on hand-puppeteering. Thirteen subjects are recruited to design a forearm waving gesture for a NAO robot by the two methods. The obtained two groups of forearm waving gestures are then compared by another sixteen subjects. Our experimental results indicate that the forearm waving gestures obtained from the hand-puppeteering method are slower and have smaller range of motion than those obtained from the motion mapping method. Besides, people tend to perceive the forearm waving gestures obtained from the hand-puppeteering method as more likeable and as conveying the greeting message better than those obtained from the motion mapping method. This work contributes to a better understanding of the nature of the two gesture design methods and offers instructive reference for robot behavior designers on design method choosing.

In this paper we provide empirical evidence that using humanlike gaze cues during human-robot handovers can improve the timing and perceived quality of the handover event. Handovers serve as the foundation of many human-robot tasks. Fluent, legible handover interactions require appropriate nonverbal cues to signal handover intent, location and timing. Inspired by observations of human-human handovers, we implemented gaze behaviors on a PR2 humanoid robot. The robot handed over water bottles to a total of 102 naïve subjects while varying its gaze behaviour: no gaze, gaze designed to elicit shared attention at the handover location, and the shared attention gaze complemented with a turn-taking cue. We compared subject perception of and reaction time to the robot-initiated handovers across the three gaze conditions. Results indicate that subjects reach for the offered object significantly earlier when a robot provides a shared attention gaze cue during a handover. We also observed a statistical trend of subjects preferring handovers with turn-taking gaze cues over the other conditions. Our work demonstrates that gaze can play a key role in improving user experience of human-robot handovers, and help make handovers fast and fluent.

This paper proposed a design of an endoscopic stitching device for surgical obesity treatment using a N.O.T.E.S (Natural Orifice Translumenal Endoscopic Surgery) approach. This proposed N.O.T.E.S gastroplasty approach performs stitching and resizing of a stomach from inside, aiming at further reducing postoperative complications by avoiding the use of skin incisions. The presented design can be inserted into the stomach in a folded configuration and be unfolded into a working configuration to perform stitching. It uses pre-curved super-elastic NiTi (Nickel-Titanium) alloy needle to facilitate the stitching motion. Role of the NiTi needle is demonstrated in in-vitro tissue penetrating experiments, while deployment and stitching motions of this device are verified using simulations.