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A doubly degenerate parabolic equation in non-divergent form with variable growth is investigated in this paper. In suitable spaces, we prove the existence of weak solutions of the equation for cases $1\leq m < 2$ and $m\geq 2$ in different ways. And we establish the non-expansion of support of the solution for the problem.

We focus on the task of unknown object rearrangement, where a robot is supposed to re-configure the objects into a desired goal configuration specified by an RGB-D image. Recent works explore unknown object rearrangement systems by incorporating learning-based perception modules. However, they are sensitive to perception error, and pay less attention to task-level performance. In this paper, we aim to develop an effective system for unknown object rearrangement amidst perception noise. We theoretically reveal that the noisy perception impacts grasp and place in a decoupled way, and show such a decoupled structure is valuable to improve task optimality. We propose GSP, a dual-loop system with the decoupled structure as prior. For the inner loop, we learn a see policy for self-confident in-hand object matching. For the outer loop, we learn a grasp policy aware of object matching and grasp capability guided by task-level rewards. We leverage the foundation model CLIP for object matching, policy learning and self-termination. A series of experiments indicate that GSP can conduct unknown object rearrangement with higher completion rates and fewer steps., Comment: Accepted by T-RO

The twig edge states in graphene-like structures are viewed as the fourth states complementary to their zigzag, bearded, and armchair counterparts. In this work, we study a rod-in-plasma system in honeycomb lattice with twig edge truncation under external magnetic fields and lattice scaling and show that twig edge states can exist in different phases of the system, such as quantum Hall phase, quantum spin Hall phase and insulating phase. The twig edge states in the negative permittivity background exhibit robust one-way transmission property immune to backscattering and thus provide a novel avenue for solving the plasma communication blackout problem. Moreover, we demonstrate that corner and edge states can exist within the shrunken structure by modulating the on-site potential of the twig edges. Especially, helical edge states with the unique feature of pseudospin-momentum locking that could be excited by chiral sources are demonstrated at the twig edges. Our results show that the twig edges and interface engineering can bring new opportunities for more flexible manipulation of electromagnetic waves.

This paper introduces a learning-based framework for robot adaptive manipulating the object with a revolute joint in unstructured environments. We concentrate our discussion on various cabinet door opening tasks. To improve the performance of Deep Reinforcement Learning in this scene, we analytically provide an efficient sampling manner utilizing the constraints of the objects. To open various kinds of doors, we add encoded environment parameters that define the various environments to the input of out policy. To transfer the policy into the real world, we train an adaptation module in simulation and fine-tune the adaptation module to cut down the impact of the policy-unaware environment parameters. We design a series of experiments to validate the efficacy of our framework. Additionally, we testify to the model's performance in the real world compared to the traditional door opening method.

Recently, several works achieve end-to-end visual servoing (VS) for robotic manipulation by replacing traditional controller with differentiable neural networks, but lose the ability to servo arbitrary desired poses. This letter proposes a differentiable architecture for arbitrary pose servoing: a hyper-network based neural controller (HPN-NC). To achieve this, HPN-NC consists of a hyper net and a low-level controller, where the hyper net learns to generate the parameters of the low-level controller and the controller uses the 2D keypoints error for control like traditional image-based visual servoing (IBVS). HPN-NC can complete 6 degree of freedom visual servoing with large initial offset. Taking advantage of the fully differentiable nature of HPN-NC, we provide a three-stage training procedure to servo real world objects. With self-supervised end-to-end training, the performance of the integrated model can be further improved in unseen scenes and the amount of manual annotations can be significantly reduced.

Plasma photonic crystals designed in this paper are composed of gas discharge tubes to control the flow of electromagnetic waves. The band structures calculated by the finite element method are consistent with the experimental results in that two distinct attenuation peaks appear in the ranges of 1 GHz to 2.5 GHz and 5 GHz to 6 GHz. Electromagnetic parameters of the plasma are extracted by the Nicolson-Ross-Weir method and effective medium theory. The measured electron density is between 1e11 cm-3 and 1e12 cm-3, which verifies the correctness of the parameter used in the simulation, and the collision frequency is near 1.5e10 Hz. As the band structures are corroborated by the measured scattering parameters, we introduce the concept of photonic topological insulator based on the quantum Valley Hall effect into the plasma photonic crystal. A valley-dependent plasma photonic crystal with hexagonal lattice is constructed, and the phase transition of the valley K(K') occurs by breaking the spatial inversion symmetry. Valley-spin locked topological edge states are generated and excited by chiral sources. The frequency of the non-bulk state can be dynamically regulated by the electron density. This concept paves the way for novel, tunable topological edge states. More interestingly, the Dirac cone is broken when the electron density increases to 3.1e12 cm-3, which distinguishes from the methods of applying a magnetic field and changing the symmetry of the point group.

We focus on the task of language-conditioned grasping in clutter, in which a robot is supposed to grasp the target object based on a language instruction. Previous works separately conduct visual grounding to localize the target object, and generate a grasp for that object. However, these works require object labels or visual attributes for grounding, which calls for handcrafted rules in planner and restricts the range of language instructions. In this paper, we propose to jointly model vision, language and action with object-centric representation. Our method is applicable under more flexible language instructions, and not limited by visual grounding error. Besides, by utilizing the powerful priors from the pre-trained multi-modal model and grasp model, sample efficiency is effectively improved and the sim2real problem is relived without additional data for transfer. A series of experiments carried out in simulation and real world indicate that our method can achieve better task success rate by less times of motion under more flexible language instructions. Moreover, our method is capable of generalizing better to scenarios with unseen objects and language instructions. Our code is available at https://github.com/xukechun/Vision-Language-Grasping, Comment: Accepted by ICRA 2023

Detecting both known and unknown objects is a fundamental skill for robot manipulation in unstructured environments. Open-set object detection (OSOD) is a promising direction to handle the problem consisting of two subtasks: objects and background separation, and open-set object classification. In this paper, we present Openset RCNN to address the challenging OSOD. To disambiguate unknown objects and background in the first subtask, we propose to use classification-free region proposal network (CF-RPN) which estimates the objectness score of each region purely using cues from object's location and shape preventing overfitting to the training categories. To identify unknown objects in the second subtask, we propose to represent them using the complementary region of known categories in a latent space which is accomplished by a prototype learning network (PLN). PLN performs instance-level contrastive learning to encode proposals to a latent space and builds a compact region centering with a prototype for each known category. Further, we note that the detection performance of unknown objects can not be unbiasedly evaluated on the situation that commonly used object detection datasets are not fully annotated. Thus, a new benchmark is introduced by reorganizing GraspNet-1billion, a robotic grasp pose detection dataset with complete annotation. Extensive experiments demonstrate the merits of our method. We finally show that our Openset RCNN can endow the robot with an open-set perception ability to support robotic rearrangement tasks in cluttered environments. More details can be found in https://sites.google.com/view/openset-rcnn/, Comment: IEEE Robotics and Automation Letters

In the peg insertion task, human pays attention to the seam between the peg and the hole and tries to fill it continuously with visual feedback. By imitating the human behavior, we design architectures with position and orientation estimators based on the seam representation for pose alignment, which proves to be general to the unseen peg geometries. By putting the estimators into the closed-loop control with reinforcement learning, we further achieve a higher or comparable success rate, efficiency, and robustness compared with the baseline methods. The policy is trained totally in simulation without any manual intervention. To achieve sim-to-real, a learnable segmentation module with automatic data collecting and labeling can be easily trained to decouple the perception and the policy, which helps the model trained in simulation quickly adapt to the real world with negligible effort. Results are presented in simulation and on a physical robot. Code, videos, and supplemental material are available at https://github.com/xieliang555/SFN.git, Comment: 6 pages; accepted to IEEE International Conference on Robotics and Automation 2022 (ICRA 2022)

The high recombination rate of photogenerated carriers is the bottleneck of photocatalysis, severely limiting the photocatalytic efficiency. Here, we develop a dipole-scheme (D-scheme for short) photocatalytic model and materials realization. The D-scheme heterojunction not only can effectively separate electrons and holes by a large polarization field, but also boosts photocatalytic redox reactions with large driving photovoltages and without any carrier loss. By means of first-principles and GW calculations, we propose a D-scheme heterojunction prototype with two real polar materials, PtSeTe/LiGaS2. This D-scheme photocatalyst exhibits a high capability of the photogenerated carrier separation and near-infrared light absorption. Moreover, our calculations of the Gibbs free energy imply a high ability of the hydrogen and oxygen evolution reaction by a large driving force. The proposed D-scheme photocatalytic model is generalized and paves a valuable route of significantly improving the photocatalytic efficiency., Comment: 10 pages, 5 figures

Object 6D pose estimation is a fundamental task in many applications. Conventional methods solve the task by detecting and matching the keypoints, then estimating the pose. Recent efforts bringing deep learning into the problem mainly overcome the vulnerability of conventional methods to environmental variation due to the hand-crafted feature design. However, these methods cannot achieve end-to-end learning and good interpretability at the same time. In this paper, we propose REDE, a novel end-to-end object pose estimator using RGB-D data, which utilizes network for keypoint regression, and a differentiable geometric pose estimator for pose error back-propagation. Besides, to achieve better robustness when outlier keypoint prediction occurs, we further propose a differentiable outliers elimination method that regresses the candidate result and the confidence simultaneously. Via confidence weighted aggregation of multiple candidates, we can reduce the effect from the outliers in the final estimation. Finally, following the conventional method, we apply a learnable refinement process to further improve the estimation. The experimental results on three benchmark datasets show that REDE slightly outperforms the state-of-the-art approaches and is more robust to object occlusion.

We propose a novel excellent two-dimensional photocatalyst SnN3 monolayer using first-principles calculations. The stability of SnN3 monolayer have been examined via formation energy, phonon spectrum and ab initio molecular dynamics calculations. Large optical absorption capacity plays significant role in the enhancement of photocatalytic splitting of water. The SnN3 monolayer have ultra-high optical absorption capacity in visible region, which is as three and four times as that of SnP3 and MoS2 monolayer, respectively. Available potential and appropriate band positions indicating the ability of overall water splitting even in a wide strain range. Electronic properties of SnN3 monolayer can also be engineered effectively via the external strain, such as the conversion from in-direct band gap to direct band gap. The applied electric field splits the energy levels due to Stark effect, resulting in states accumulation and smaller gap width, Comment: 21 pages,6 figures

This paper develops a general approach to the derivation of the boundary conditions for hydrodynamic equations for charged and neutral plasma components. It includes both a well-known classical case for pure diffusion, and considers the expressions for diffusion and drift together - for an absorbing (neutralizing) wall with partial reflection and the possible emission of plasma components. Some unclear and controversial terms found in the existing literature are clarified. Several examples of applications of the results, which illustrate the properties of boundary conditions for electrons and ions, are calculated and analyzed., Comment: This is a revised version of our paper that was primarelly submitted to Physical Review E - 2018, November, 24. Now it is published in Physics of Plasmas 27, 1 (2020). After publication, a mistake in the Sample IV.C is corrected