Optimization Model for Planning Precision Grasps with Multi-Fingered Hands

Precision grasps with multi-fingered hands are important for precise placement and in-hand manipulation tasks. Searching precision grasps on the object represented by point cloud, is challenging due to the complex object shape, high-dimensionality, collision and undesired properties of the sensing and positioning. This paper proposes an optimization model to search for precision grasps with multi-fingered hands. The model takes noisy point cloud of the object as input and optimizes the grasp quality by iteratively searching for the palm pose and finger joints positions. The collision between the hand and the object is approximated and penalized by a series of least-squares. The collision approximation is able to handle the point cloud representation of the objects with complex shapes. The proposed optimization model is able to locate collision-free optimal precision grasps efficiently. The average computation time is 0.50 sec/grasp. The searching is robust to the incompleteness and noise of the point cloud. The effectiveness of the algorithm is demonstrated by experiments.Comment: Submitted to IROS2019, experiment on BarrettHand, 8 page

Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration

Personalized federated learning (PFL) reduces the impact of non-independent and identically distributed (non-IID) data among clients by allowing each client to train a personalized model when collaborating with others. A key question in PFL is to decide which parameters of a client should be localized or shared with others. In current mainstream approaches, all layers that are sensitive to non-IID data (such as classifier layers) are generally personalized. The reasoning behind this approach is understandable, as localizing parameters that are easily influenced by non-IID data can prevent the potential negative effect of collaboration. However, we believe that this approach is too conservative for collaboration. For example, for a certain client, even if its parameters are easily influenced by non-IID data, it can still benefit by sharing these parameters with clients having similar data distribution. This observation emphasizes the importance of considering not only the sensitivity to non-IID data but also the similarity of data distribution when determining which parameters should be localized in PFL. This paper introduces a novel guideline for client collaboration in PFL. Unlike existing approaches that prohibit all collaboration of sensitive parameters, our guideline allows clients to share more parameters with others, leading to improved model performance. Additionally, we propose a new PFL method named FedCAC, which employs a quantitative metric to evaluate each parameter's sensitivity to non-IID data and carefully selects collaborators based on this evaluation. Experimental results demonstrate that FedCAC enables clients to share more parameters with others, resulting in superior performance compared to state-of-the-art methods, particularly in scenarios where clients have diverse distributions.Comment: Accepted by ICCV202

Diff-Transfer: Model-based Robotic Manipulation Skill Transfer via Differentiable Physics Simulation

The capability to transfer mastered skills to accomplish a range of similar yet novel tasks is crucial for intelligent robots. In this work, we introduce $\textit{Diff-Transfer}$, a novel framework leveraging differentiable physics simulation to efficiently transfer robotic skills. Specifically, $\textit{Diff-Transfer}$ discovers a feasible path within the task space that brings the source task to the target task. At each pair of adjacent points along this task path, which is two sub-tasks, $\textit{Diff-Transfer}$ adapts known actions from one sub-task to tackle the other sub-task successfully. The adaptation is guided by the gradient information from differentiable physics simulations. We propose a novel path-planning method to generate sub-tasks, leveraging $Q$-learning with a task-level state and reward. We implement our framework in simulation experiments and execute four challenging transfer tasks on robotic manipulation, demonstrating the efficacy of $\textit{Diff-Transfer}$ through comprehensive experiments. Supplementary and Videos are on the website https://sites.google.com/view/difftransfe

Rain Streak Removal via Dual Graph Convolutional Network

Deep convolutional neural networks (CNNs) have become dominant in the single image de-raining area. However, most deep CNNs-based de-raining methods are designed by stacking vanilla convolutional layers, which can only be used to model local relations. Therefore, long-range contextual information is rarely considered for this specific task. To address the above problem, we propose a simple yet effective dual graph convolutional network (GCN) for single image rain removal. Specifically, we design two graphs to perform global relational modeling and reasoning. The first GCN is used to explore global spatial relations among pixels in feature maps, while the second GCN models the global relations across the channels. Compared to standard convolutional operations, the proposed two graphs enable the network to extract representations from new dimensions. To achieve the image rain removal, we further embed these two graphs and multi-scale dilated convolution into a symmetrically skip-connected network architecture. Therefore, our dual graph convolutional network is able to well handle complex and spatially long rain streaks by exploring multiple representations, e.g., multi-scale local feature, global spatial coherence and cross-channel correlation. Meanwhile, our model is easy to implement, end-to-end trainable and computationally efficient. Extensive experiments on synthetic and real data demonstrate that our method achieves significant improvements over the recent state-of-the-art methods

The Nitrogen Removal Performance and Functional Bacteria in Heterotrophic Denitrification and Mixotrophic Denitrification Process

The heterotrophic and autotrophic synergistic denitrification (HAD) system can effectively remove sulfide, nitrate, and organic carbon pollutants from municipal wastewater. However, the effect of sulfide on the functional bacteria in the denitrification system is still unclear. To better understand the mechanism of sulfide affected on bacteria in the system, the up-flow anaerobic sludge blanket (UASB) reactor was operated continuously under heterotrophic (no sulfide added) and mixotrophic conditions (with increased sulfide contents) for 120 days. The contents of protein (PN) in extracellular polymeric substances (EPS) were significantly increased with the addition of sulfide, which enhanced the flocculation of sludge and was beneficial to the formation of high-density microorganism communities. The dominant bacteria showed large differences under different nutrient conditions. The abundances of Thauera increased from 4.13% to over 12.94%, and that of Dechloromonas and Thiobacillus were 2.61–3.01% and 1.04–2.66% respectively after added sulfide. And the efficient performance of the system in mixotrophic conditions was accomplished with the interaction of heterotrophic sulfide-oxidizing, nitrate-reducing bacteria (Thauera, Dechloromonas), autotrophic sulfide-oxidizing, nitrate-reducing bacteria (Thiobacillus) and heterotrophic nitrate-reducing bacteria (Rubrivivax, Acidovorax, Simplicispira, Alicycliphilus). Moreover, the abundances of Nar G, Nap A, Nir S, Nor B, and Nos Z were significantly enhanced in mixotrophic conditions, indicating that the nitrogen metabolism potential of the system was also improved after added sulfide. These results elucidated the reasons for the enhanced denitrifying capacity of the system by adding S2− from the microbiological point of view and provided a theoretical basis for the establishment of an efficient denitrification system