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Finding an high-quality solution for the tabletop object rearrangement planning is a challenging problem. Compared to determining a goal arrangement, rearrangement planning is challenging due to the dependencies between objects and the buffer capacity available to hold objects. Although orla* has proposed an A* based searching strategy with lazy evaluation for the high-quality solution, it is not scalable, with the success rate decreasing as the number of objects increases. To overcome this limitation, we propose an enhanced A*-based algorithm that improves state representation and employs incremental goal attempts with lazy evaluation at each iteration. This approach aims to enhance scalability while maintaining solution quality. Our evaluation demonstrates that our algorithm can provide superior solutions compared to orla*, in a shorter time, for both stationary and mobile robots.

Unraveling the complexities of nonlinear optical (NLO) responses, particularly the intricate many-body interactions among photons, electrons, and phonons, remains a significant challenge in condensed matter physics. Here, we present a diagrammatic approach to explore NLO responses with electron-phonon coupling (EPC), focusing on the phonon-mediated nonlinear optical (Ph-NLO) responses up to the second order in photon perturbation. We systematically analyze the shift and ballistic mechanisms responsible for phonon-mediated electron-photon interactions. By incorporating EPC effects, we elucidate phenomena such as phonon-mediated shift current (Ph-SC) and second-harmonic generation (Ph-SHG) in a comprehensive Ph-NLO framework. This approach enables below-gap resonant responses at terahertz photon frequencies, offering a promising mechanism for terahertz optical applications that surpasses the constraints of conventional pure-electronic NLO theories. Additionally, we explore the geometric and topological consequences of Ph-NLO responses by introducing the EPC Berry curvature, EPC quantum metric, and EPC shift vector. These concepts unveil a unique quantum geometric structure within the Hilbert space, parameterized by both the electronic wavevector and phonon-displacement, thereby extending the established pure-electronic quantum geometry. Using a general Rice-Mele model, we demonstrate the connection between Ph-NLO responses and EPC geometry, discussing the implications and predicting observable effects for future experimental validation. This framework also provides a foundation for advancing first-principles calculations aimed at the discovery and engineering of NLO materials. The insights gained from this study contribute to a more profound understanding of NLO responses and EPC quantum geometry., Comment: 24 pages, 16 figures

Current multimodal and multitask foundation models like 4M or UnifiedIO show promising results, but in practice their out-of-the-box abilities to accept diverse inputs and perform diverse tasks are limited by the (usually rather small) number of modalities and tasks they are trained on. In this paper, we expand upon the capabilities of them by training a single model on tens of highly diverse modalities and by performing co-training on large-scale multimodal datasets and text corpora. This includes training on several semantic and geometric modalities, feature maps from recent state of the art models like DINOv2 and ImageBind, pseudo labels of specialist models like SAM and 4DHumans, and a range of new modalities that allow for novel ways to interact with the model and steer the generation, for example image metadata or color palettes. A crucial step in this process is performing discrete tokenization on various modalities, whether they are image-like, neural network feature maps, vectors, structured data like instance segmentation or human poses, or data that can be represented as text. Through this, we expand on the out-of-the-box capabilities of multimodal models and specifically show the possibility of training one model to solve at least 3x more tasks/modalities than existing ones and doing so without a loss in performance. This enables more fine-grained and controllable multimodal generation capabilities and allows us to study the distillation of models trained on diverse data and objectives into a unified model. We successfully scale the training to a three billion parameter model using tens of modalities and different datasets. The resulting models and training code are open sourced at 4m.epfl.ch., Comment: Project page at 4m.epfl.ch

We consider the problem of building an assistive robotic system that can help humans in daily household cleanup tasks. Creating such an autonomous system in real-world environments is inherently quite challenging, as a general solution may not suit the preferences of a particular customer. Moreover, such a system consists of multi-objective tasks comprising -- (i) Detection of misplaced objects and prediction of their potentially correct placements, (ii) Fine-grained manipulation for stable object grasping, and (iii) Room-to-room navigation for transferring objects in unseen environments. This work systematically tackles each component and integrates them into a complete object rearrangement pipeline. To validate our proposed system, we conduct multiple experiments on a real robotic platform involving multi-room object transfer, user preference-based placement, and complex pick-and-place tasks. Project page: https://sites.google.com/eng.ucsd.edu/home-robot, Comment: Paper accepted at IEEE IRC-2023

The quasiparticle method, which has achieved a great success in solid state electronics and crystal lattice vibration, is adopted to study the solid state ion transport. A general recipe is put forward to map the classical solid state ion system together with the short-range ion-ion repulsion to a quasiparticle quantum system based on which simple tight-binding models are set up to study popular solid state ionic issues. The derived ionic eigen-states and transport can naturally incorporate the concerted behavior. The influence of mobile ion doping to ionic conductivity is investigated by an explicit mean-field expression of ion-ion Coulomb interaction to reveal the mechanism of super-ionic conduction. Similar trick is also applied to study the the Onsager transport originated from ion-electron interference. The so called high-entropy mechanism is also explored where the lattice random distorsion and multi-site percolation exhibit positive influence to increase ionic conductivity. Our method is expected to provide a novel framework to understand and study the solid state ion transport., Comment: The acknowledgement section was missed. The authors need to check the acknowledgement list before resubmitting to arXiv. By now the acknowledgement section includes: We thank the insightful discussions with Dr. Qiyang Lu, Dr. Wei Zhu, Dr. Congjun Wu, Dr. Shi Liu, Dr. Yizhou Zhu and Dr. Wenbin Li of Westlake University

Due to their complexity, foliated structure problems often pose intricate challenges to task and motion planning in robotics manipulation. To counter this, our study presents the ``Foliated Repetition Roadmap.'' This roadmap assists task and motion planners by transforming the complex foliated structure problem into a more accessible graph format. By leveraging query experiences from different foliated manifolds, our framework can dynamically and efficiently update this graph. The refined graph can generate distribution sets, optimizing motion planning performance in foliated structure problems. In our paper, we lay down the theoretical groundwork and illustrate its practical applications through real-world examples.

In this article, we systematically explore several key properties of electronic states in helical materials systems, including the inheritance of orbital angular momentum (OAM) from local atomic orbitals to the entire helical structure, the conservation of helical momentum, and the emergence of helical-induced spin-orbit coupling (hSOC). We then apply this comprehensive theoretical framework to elucidate the electronic structure of one-dimensional (1D) helical crystal InSeI. Our analysis reveals the influence of hSOC, evident in spin-mixing energy gaps within the electronic band structure, as calculated through density functional theory. Utilizing a combination of tight-binding modeling and first-principles calculations, we ascertain the spin-polarized electric response and the chiral-switchable second-order photocurrent response of InSeI, characterized as the Landauer-Buttiker ballistic transport and shift current response. The results highlight the potential of 1D InSeI for applications in spintronics and optoelectronics. The overarching theoretical framework established in this work will prove invaluable for the investigation of other helical electronic systems., Comment: 14 pages, 7 figures

Nowadays, a number of grasping algorithms have been proposed, that can predict a candidate of grasp poses, even for unseen objects. This enables a robotic manipulator to pick-and-place such objects. However, some of the predicted grasp poses to stably lift a target object may not be directly approachable due to workspace limitations. In such cases, the robot will need to re-grasp the desired object to enable successful grasping on it. This involves planning a sequence of continuous actions such as sliding, re-grasping, and transferring. To address this multi-modal problem, we propose a Markov-Decision Process-based multi-modal planner that can rearrange the object into a position suitable for stable manipulation. We demonstrate improved performance in both simulation and the real world for pick-and-place tasks.

Spin-orbit coupling in chiral materials can induce chirality-dependent spin splitting, enabling electrical manipulation of spin polarization. Here, we use first-principles calculations to investigate the electronic states of chiral one-dimensional (1D) semiconductor InSeI, which has two enantiomorphic configurations with left- and right-handedness. We find that opposite spin states exist in the left- and right-handed 1D InSeI with significant spin splitting and spin-momentum collinear locking. Although the spin states at the conduction band minimum (CBM) and valence band maximum (VBM) of 1D InSeI are both nearly degenerate, a direct-to-indirect bandgap transition occurs when a moderate tensile strain ($\sim$4%) is applied along the 1D chain direction, leading to a sizable spin splitting ($\sim$0.11 eV) at the CBM. These findings indicate that 1D InSeI is a promising material for chiral spintronics., Comment: 6 pages, 3 figures

The World Robot Summit (WRS) 2020 Assembly Challenge is designed to allow teams to demonstrate how one can build flexible, robust systems for assembly of machined objects. We present our approach to assembly based on integration of machine vision, robust planning and execution using behavior trees and a hierarchy of recovery strategies to ensure robust operation. Our system was selected for the WRS 2020 Assembly Challenge finals based on robust performance in the qualifying rounds. We present the systems approach adopted for the challenge., Comment: 8 pages

The World Robotics Challenge (2018 & 2020) was designed to challenge teams to design systems that are easy to adapt to new tasks and to ensure robust operation in a semi-structured environment. We present a layered strategy to transform missions into tasks and actions and provide a set of strategies to address simple and complex failures. We propose a model for characterizing failures using this model and discuss repairs. Simple failures are by far the most common in our WRC system and we also present how we repaired them.

In the robotic industry, specular and textureless metallic components are ubiquitous. The 6D pose estimation of such objects with only a monocular RGB camera is difficult because of the absence of rich texture features. Furthermore, the appearance of specularity heavily depends on the camera viewpoint and environmental light conditions making traditional methods, like template matching, fail. In the last 30 years, pose estimation of the specular object has been a consistent challenge, and most related works require massive knowledge modeling effort for light setups, environment, or the object surface. On the other hand, recent works exhibit the feasibility of 6D pose estimation on a monocular camera with convolutional neural networks(CNNs) however they mostly use opaque objects for evaluation. This paper provides a data-driven solution to estimate the 6D pose of specular objects for grasping them, proposes a cost function for handling symmetry, and demonstrates experimental results showing the system's feasibility., Comment: submitted to ICRA 2021