A two-stage grasp detection method for sequential robotic grasping in stacking scenarios.

Dexterous grasping is essential for the fine manipulation tasks of intelligent robots; however, its application in stacking scenarios remains a challenge. In this study, we aimed to propose a two-phase approach for grasp detection of sequential robotic grasping, specifically for application in stacking scenarios. In the initial phase, a rotated-YOLOv3 (R-YOLOv3) model was designed to efficiently detect the category and position of the top-layer object, facilitating the detection of stacked objects. Subsequently, a stacked scenario dataset with only the top-level objects annotated was built for training and testing the R-YOLOv3 network. In the next phase, a G-ResNet50 model was developed to enhance grasping accuracy by finding the most suitable pose for grasping the uppermost object in various stacking scenarios. Ultimately, a robot was directed to successfully execute the task of sequentially grasping the stacked objects. The proposed methodology demonstrated the average grasping prediction success rate of 96.60% as observed in the Cornell grasping dataset. The results of the 280 real-world grasping experiments, conducted in stacked scenarios, revealed that the robot achieved a maximum grasping success rate of 95.00%, with an average handling grasping success rate of 83.93%. The experimental findings demonstrated the efficacy and competitiveness of the proposed approach in successfully executing grasping tasks within complex multi-object stacked environments.

Exploring density rectification and domain adaption method for crowd counting.

Crowd counting has received increasing attention due to its important roles in multiple fields, such as social security, commercial applications, epidemic prevention and control. To this end, we explore two critical issues that seriously affect the performance of crowd counting including nonuniform crowd density distribution and cross-domain problems. Aiming at the nonuniform crowd density distribution issue, we propose a density rectifying network (DRNet) that consists of several dual-layer pyramid fusion modules (DPFM) and a density rectification map (DRmap) auxiliary learning module. The proposed DPFM is embedded into DRNet to integrate multi-scale crowd density features through dual-layer pyramid fusion. The devised DRmap auxiliary learning module further rectifies the incorrect crowd density estimation by adaptively weighting the initial crowd density maps. With respect to the cross-domain issue, we develop a domain adaptation method of randomly cutting mixed dual-domain images, which learns domain-invariance features and decreases the domain gap between the source domain and the target domain from global and local perspectives. Experimental results indicate that the devised DRNet achieves the best mean absolute error (MAE) and competitive mean squared error (MSE) compared with other excellent methods on four benchmark datasets. Additionally, a series of cross-domain experiments are conducted to demonstrate the effectiveness of the proposed domain adaption method. Significantly, when the A and B parts of the Shanghaitech dataset are the source domain and target domain respectively, the proposed domain adaption method decreases the MAE of DRNet by 47.6 % .

Partially observable Markov decision processes and performance sensitivity analysis.

The sensitivity-based optimization of Markov systems has become an increasingly important area. From the perspective of performance sensitivity analysis, policy-iteration algorithms and gradient estimation methods can be directly obtained for Markov decision processes (MDPs). In this correspondence, the sensitivity-based optimization is extended to average reward partially observable MDPs (POMDPs). We derive the performance-difference and performance-derivative formulas of POMDPs. On the basis of the performance-derivative formula, we present a new method to estimate the performance gradients. From the performance-difference formula, we obtain a sufficient optimality condition without the discounted reward formulation. We also propose a policy-iteration algorithm to obtain a nearly optimal finite-state-controller policy.

Power-law strength-degree correlation from resource-allocation dynamics on weighted networks.

Many weighted scale-free networks are known to have a power-law correlation between strength and degree of nodes, which, however, has not been well explained. We investigate the dynamic behavior of resource-traffic flow on scale-free networks. The dynamical system will evolve into a kinetic equilibrium state, where the strength, defined by the amount of resource or traffic load, is correlated with the degree in a power-law form with tunable exponent. The analytical results agree well with simulations.