arXiv subjects

Shiyang Lu

Publications and source records attributed to Shiyang Lu.

16 recordsLinked to original sources

arXiv2025

Single femtosecond laser pulse-driven ferromagnetic switching

Light pulses offer a faster, more energy-efficient, and direct route to magnetic bit writing, pointing toward a hybrid memory and computing paradigm based on photon transmission and spin retention. Yet progress remains hindered, as deterministic, single-pulse optical toggle switching has so far been achieved only with ferrimagnetic materials, which require too specific a rare-earth composition and temperature conditions for technological use. In mainstream ferromagnet--central to spintronic memory and storage--such bistable switching is considered fundamentally difficult, as laser-induced heating does not inherently break time-reversal symmetry. Here, we report coherent magnetization switching in ferromagnets, driven by thermal anisotropy torque with single laser pulses. The toggle switching behavior is robust over a broad range of pulse durations, from femtoseconds to picoseconds, a prerequisite for practical applications. Furthermore, the phenomenon exhibits reproducibility in CoFeB/MgO-based magnetic tunnel junctions with a high magnetoresistance exceeding 110%, as well as the scalability down to nanoscales with remarkable energy efficiency (17 fJ per 100-nm-sized bit). These results mark a notable step toward integrating opto-spintronics into next-generation memory and storage technologies.

cond-mat.mes-hall↗

arXiv2025

Nonlinearity Modulation of Auto-oscillations in Three-terminal Magnetic Tunnel Junctions

Spin torque nano-oscillators (STNOs) hold encouraging promise for nanoscale microwave generators, modulators, and new types of intelligent computing. The nonlinearity, describing the current-induced tunability of oscillating frequency, is a distinctive feature of STNOs, which plays important roles in efficient manipulation of microwave frequencies, rapid spec-trum analysis, and the design of neuromorphic devices. However, experimental research on its efficient modulation remains limited. Here, we comprehensively studied the impact of several factors on nonlinearity in nanoscale three-terminal MTJ-STNOs, including the external magnetic field, the thickness of CoFeB free layer, and the combination of spin-transfer torque (STT) and spin-orbit torque (SOT). Among these factors, nonlinearity can be significantly tuned by the direction of magnetic field as well as the thickness of CoFeB free layer. Notably, it reaches zero in 1.1 nm CoFeB, where the oscillation frequency is not affected by the drive current. Such property provides a more intrinsic and robust approach to achieve zero nonlinearity in STNOs, which is advantageous for high-quality microwave generators. More importantly, we found that nonlinearity can also be electrically modulated by both STT and SOT currents, and develop a refined model that accounts for the additional contribution of the SOT current to explain the mechanism. This electrical approach is more convenient, energy-efficient, and well-suited for miniaturization. Our findings offer a comprehensive understanding and open up a new dimension for the current tunability of nonlinearity in MTJ-STNOs, benefiting further optimization in nanoscale STNO-based microwave generators and neuromorphic computing devices.

physics.app-ph↗

arXiv2025

Giant Orbital Torque-driven Picosecond Switching in Magnetic Tunnel Junctions

Orbital Hall effect was recently discovered as a novel pathway for driving magnetic moment. However, the integration of orbital Hall effect in magnetic memories suffers from low orbital-to-spin conversion efficiency and incompatibility with magnetic tunnel junctions. Here we demonstrate an orbital Hall effect-driven magnetic tunnel junction based on Ru/W bilayer, where the Ru layer possesses a strong orbital Hall conductivity and the {\alpha}-W layer features an orbital-to-spin conversion efficiency exceeding 90% because of the large orbit-spin diffusivity. By harnessing the giant orbital torque, we achieve a 28.7-picosecond switching and a five to eight-fold reduction in driving voltages over conventional spin-orbit torque magnetic memories. Our work bridges the critical gap between orbital effects and magnetic memory applications, significantly advancing the field of spintronics and orbitronics.

cond-mat.mtrl-sci↗

arXiv2025

Impact-resistant, autonomous robots inspired by tensegrity architecture

Future robots will navigate perilous, remote environments with resilience and autonomy. Researchers have proposed building robots with compliant bodies to enhance robustness, but this approach often sacrifices the autonomous capabilities expected of rigid robots. Inspired by tensegrity architecture, we introduce a tensegrity robot -- a hybrid robot made from rigid struts and elastic tendons -- that demonstrates the advantages of compliance and the autonomy necessary for task performance. This robot boasts impact resistance and autonomy in a field environment and additional advances in the state of the art, including surviving harsh impacts from drops (at least 5.7 m), accurately reconstructing its shape and orientation using on-board sensors, achieving high locomotion speeds (18 bar lengths per minute), and climbing the steepest incline of any tensegrity robot (28 degrees). We characterize the robot's locomotion on unstructured terrain, showcase its autonomous capabilities in navigation tasks, and demonstrate its robustness by rolling it off a cliff.

cs.RO↗

arXiv2024

Anatomy of Thermally Interplayed Spin-Orbit Torque Driven Antiferromagnetic Switching

Current-induced antiferromagnetic (AFM) switching remains critical in spintronics, yet the interplay between thermal effects and spin torques still lacks clear clarification. Here we experimentally investigate the thermally interplayed spin-orbit torque induced AFM switching in magnetic tunnel junctions via pulse-width dependent reversal and time-resolved measurements. By introducing the Langevin random field into the AFM precession equation, we establish a novel AFM switching model that anatomically explains the experimental observations. Our findings elucidate the currentinduced AFM switching mechanism and offer significant promise for advancements in spintronics.

cond-mat.mes-hall↗

arXiv2023

OVIR-3D: Open-Vocabulary 3D Instance Retrieval Without Training on 3D Data

This work presents OVIR-3D, a straightforward yet effective method for open-vocabulary 3D object instance retrieval without using any 3D data for training. Given a language query, the proposed method is able to return a ranked set of 3D object instance segments based on the feature similarity of the instance and the text query. This is achieved by a multi-view fusion of text-aligned 2D region proposals into 3D space, where the 2D region proposal network could leverage 2D datasets, which are more accessible and typically larger than 3D datasets. The proposed fusion process is efficient as it can be performed in real-time for most indoor 3D scenes and does not require additional training in 3D space. Experiments on public datasets and a real robot show the effectiveness of the method and its potential for applications in robot navigation and manipulation.

cs.CV↗

arXiv2023

Context-Aware Entity Grounding with Open-Vocabulary 3D Scene Graphs

We present an Open-Vocabulary 3D Scene Graph (OVSG), a formal framework for grounding a variety of entities, such as object instances, agents, and regions, with free-form text-based queries. Unlike conventional semantic-based object localization approaches, our system facilitates context-aware entity localization, allowing for queries such as ``pick up a cup on a kitchen table" or ``navigate to a sofa on which someone is sitting". In contrast to existing research on 3D scene graphs, OVSG supports free-form text input and open-vocabulary querying. Through a series of comparative experiments using the ScanNet dataset and a self-collected dataset, we demonstrate that our proposed approach significantly surpasses the performance of previous semantic-based localization techniques. Moreover, we highlight the practical application of OVSG in real-world robot navigation and manipulation experiments.

cs.RO↗

arXiv2023

SOT-MRAM-Enabled Probabilistic Binary Neural Networks for Noise-Tolerant and Fast Training

We report the use of spin-orbit torque (SOT) magnetoresistive random-access memory (MRAM) to implement a probabilistic binary neural network (PBNN) for resource-saving applications. The in-plane magnetized SOT (i-SOT) MRAM not only enables field-free magnetization switching with high endurance (> 10^11), but also hosts multiple stable probabilistic states with a low device-to-device variation (< 6.35%). Accordingly, the proposed PBNN outperforms other neural networks by achieving an 18* increase in training speed, while maintaining an accuracy above 97% under the write and read noise perturbations. Furthermore, by applying the binarization process with an additional SOT-MRAM dummy module, we demonstrate an on-chip MNIST inference performance close to the ideal baseline using our SOT-PBNN hardware.

physics.app-ph↗

arXiv2023

Self-Supervised Learning of Object Segmentation from Unlabeled RGB-D Videos

This work proposes a self-supervised learning system for segmenting rigid objects in RGB images. The proposed pipeline is trained on unlabeled RGB-D videos of static objects, which can be captured with a camera carried by a mobile robot. A key feature of the self-supervised training process is a graph-matching algorithm that operates on the over-segmentation output of the point cloud that is reconstructed from each video. The graph matching, along with point cloud registration, is able to find reoccurring object patterns across videos and combine them into 3D object pseudo labels, even under occlusions or different viewing angles. Projected 2D object masks from 3D pseudo labels are used to train a pixel-wise feature extractor through contrastive learning. During online inference, a clustering method uses the learned features to cluster foreground pixels into object segments. Experiments highlight the method's effectiveness on both real and synthetic video datasets, which include cluttered scenes of tabletop objects. The proposed method outperforms existing unsupervised methods for object segmentation by a large margin.

cs.CV↗

arXiv2023

ARMBench: An Object-centric Benchmark Dataset for Robotic Manipulation

This paper introduces Amazon Robotic Manipulation Benchmark (ARMBench), a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse. Automation of operations in modern warehouses requires a robotic manipulator to deal with a wide variety of objects, unstructured storage, and dynamically changing inventory. Such settings pose challenges in perceiving the identity, physical characteristics, and state of objects during manipulation. Existing datasets for robotic manipulation consider a limited set of objects or utilize 3D models to generate synthetic scenes with limitation in capturing the variety of object properties, clutter, and interactions. We present a large-scale dataset collected in an Amazon warehouse using a robotic manipulator performing object singulation from containers with heterogeneous contents. ARMBench contains images, videos, and metadata that corresponds to 235K+ pick-and-place activities on 190K+ unique objects. The data is captured at different stages of manipulation, i.e., pre-pick, during transfer, and after placement. Benchmark tasks are proposed by virtue of high-quality annotations and baseline performance evaluation are presented on three visual perception challenges, namely 1) object segmentation in clutter, 2) object identification, and 3) defect detection. ARMBench can be accessed at http://armbench.com

cs.RO↗

arXiv2022

6N-DoF Pose Tracking for Tensegrity Robots

Tensegrity robots, which are composed of compressive elements (rods) and flexible tensile elements (e.g., cables), have a variety of advantages, including flexibility, low weight, and resistance to mechanical impact. Nevertheless, the hybrid soft-rigid nature of these robots also complicates the ability to localize and track their state. This work aims to address what has been recognized as a grand challenge in this domain, i.e., the state estimation of tensegrity robots through a markerless, vision-based method, as well as novel, onboard sensors that can measure the length of the robot's cables. In particular, an iterative optimization process is proposed to track the 6-DoF pose of each rigid element of a tensegrity robot from an RGB-D video as well as endcap distance measurements from the cable sensors. To ensure that the pose estimates of rigid elements are physically feasible, i.e., they are not resulting in collisions between rods or with the environment, physical constraints are introduced during the optimization. Real-world experiments are performed with a 3-bar tensegrity robot, which performs locomotion gaits. Given ground truth data from a motion capture system, the proposed method achieves less than 1~cm translation error and 3 degrees rotation error, which significantly outperforms alternatives. At the same time, the approach can provide accurate pose estimation throughout the robot's motion, while motion capture often fails due to occlusions.

cs.RO↗

arXiv2022

Real2Sim2Real Transfer for Control of Cable-driven Robots via a Differentiable Physics Engine

Tensegrity robots, composed of rigid rods and flexible cables, exhibit high strength-to-weight ratios and significant deformations, which enable them to navigate unstructured terrains and survive harsh impacts. They are hard to control, however, due to high dimensionality, complex dynamics, and a coupled architecture. Physics-based simulation is a promising avenue for developing locomotion policies that can be transferred to real robots. Nevertheless, modeling tensegrity robots is a complex task due to a substantial sim2real gap. To address this issue, this paper describes a Real2Sim2Real (R2S2R) strategy for tensegrity robots. This strategy is based on a differentiable physics engine that can be trained given limited data from a real robot. These data include offline measurements of physical properties, such as mass and geometry for various robot components, and the observation of a trajectory using a random control policy. With the data from the real robot, the engine can be iteratively refined and used to discover locomotion policies that are directly transferable to the real robot. Beyond the R2S2R pipeline, key contributions of this work include computing non-zero gradients at contact points, a loss function for matching tensegrity locomotion gaits, and a trajectory segmentation technique that avoids conflicts in gradient evaluation during training. Multiple iterations of the R2S2R process are demonstrated and evaluated on a real 3-bar tensegrity robot.

cs.RO↗

arXiv2022

Online Object Model Reconstruction and Reuse for Lifelong Improvement of Robot Manipulation

This work proposes a robotic pipeline for picking and constrained placement of objects without geometric shape priors. Compared to recent efforts developed for similar tasks, where every object was assumed to be novel, the proposed system recognizes previously manipulated objects and performs online model reconstruction and reuse. Over a lifelong manipulation process, the system keeps learning features of objects it has interacted with and updates their reconstructed models. Whenever an instance of a previously manipulated object reappears, the system aims to first recognize it and then register its previously reconstructed model given the current observation. This step greatly reduces object shape uncertainty allowing the system to even reason for parts of objects, which are currently not observable. This also results in better manipulation efficiency as it reduces the need for active perception of the target object during manipulation. To get a reusable reconstructed model, the proposed pipeline adopts: i) TSDF for object representation, and ii) a variant of the standard particle filter algorithm for pose estimation and tracking of the partial object model. Furthermore, an effective way to construct and maintain a dataset of manipulated objects is presented. A sequence of real-world manipulation experiments is performed. They show how future manipulation tasks become more effective and efficient by reusing reconstructed models of previously manipulated objects, which were generated during their prior manipulation, instead of treating objects as novel every time.

cs.RO↗

arXiv2020

Safe and Effective Picking Paths in Clutter given Discrete Distributions of Object Poses

Picking an item in the presence of other objects can be challenging as it involves occlusions and partial views. Given object models, one approach is to perform object pose estimation and use the most likely candidate pose per object to pick the target without collisions. This approach, however, ignores the uncertainty of the perception process both regarding the target's and the surrounding objects' poses. This work proposes first a perception process for 6D pose estimation, which returns a discrete distribution of object poses in a scene. Then, an open-loop planning pipeline is proposed to return safe and effective solutions for moving a robotic arm to pick, which (a) minimizes the probability of collision with the obstructing objects; and (b) maximizes the probability of reaching the target item. The planning framework models the challenge as a stochastic variant of the Minimum Constraint Removal (MCR) problem. The effectiveness of the methodology is verified given both simulated and real data in different scenarios. The experiments demonstrate the importance of considering the uncertainty of the perception process in terms of safe execution. The results also show that the methodology is more effective than conservative MCR approaches, which avoid all possible object poses regardless of the reported uncertainty.

cs.RO↗

arXiv2019

Enhanced Interfacial Dzyaloshinskii-Moriya Interaction in annealed Pt/Co/MgO structures

The interfacial Dzyaloshinskii-Moriya interaction (iDMI) is attracting great interests for spintronics. An iDMI constant larger than 3 mJ/m^2 is expected to minimize the size of skyrmions and to optimize the DW dynamics. In this study, we experimentally demonstrate an enhanced iDMI in Pt/Co/X/MgO ultra-thin film structures with perpendicular magnetization. The iDMI constants were measured using a field-driven creep regime domain expansion method. The enhancement of iDMI with an atomically thin insertion of Ta and Mg is comprehensively understood with the help of ab-initio calculations. Thermal annealing has been used to crystallize the MgO thin layer for improving tunneling magneto-resistance (TMR), but interestingly it also provides a further increase of the iDMI constant. An increase of the iDMI constant up to 3.3 mJ/m^2 is shown, which could be promising for the scaling down of skyrmion electronics.

cond-mat.mes-hall↗

arXiv2018

Factored Pose Estimation of Articulated Objects using Efficient Nonparametric Belief Propagation

Robots working in human environments often encounter a wide range of articulated objects, such as tools, cabinets, and other jointed objects. Such articulated objects can take an infinite number of possible poses, as a point in a potentially high-dimensional continuous space. A robot must perceive this continuous pose to manipulate the object to a desired pose. This problem of perception and manipulation of articulated objects remains a challenge due to its high dimensionality and multi-modal uncertainty. In this paper, we propose a factored approach to estimate the poses of articulated objects using an efficient nonparametric belief propagation algorithm. We consider inputs as geometrical models with articulation constraints, and observed RGBD sensor data. The proposed framework produces object-part pose beliefs iteratively. The problem is formulated as a pairwise Markov Random Field (MRF) where each hidden node (continuous pose variable) is an observed object-part's pose and the edges denote the articulation constraints between the parts. We propose articulated pose estimation by Pull Message Passing algorithm for Nonparametric Belief Propagation (PMPNBP) and evaluate its convergence properties over scenes with articulated objects.

cs.RO↗