121 accepted papers
3D Gaussian Splatting (3D-GS) has emerged as an efficient 3D representation and a promising foundation for semantic tasks like segmentation. However, existing 3D-GS-based segmentation methods typically rely on high-dimensional category features, which introduce substantial memory overhead. Moreover,
Navigating a nonholonomic robot in a cluttered, unknown environment requires accurate perception and precise motion control for real-time collision avoidance. This article presents neural proximal alternating-minimization network (NeuPAN): a real-time, highly accurate, map-free, easy-to-deploy, and
Diffusion-based policies have established a new standard for precise robotic manipulation but face a critical scalability bottleneck: high-performance models are computationally expensive, while lightweight alternatives often fail to generalize across diverse multi-task environments. Mixture-of-Expe…
Shared autonomy (SA) enables robots to infer human intent and assist in its achievement. While most research focuses on improving intent inference, it overlooks whether humans can understand the robot's intent in return. Without such mutual understanding, collaboration becomes less effective, degrad
Granular materials (GMs) are formed by a collection of particles. Even if their visual representation is straightforward, it can be seriously affected in the visually constrained environment. Based on frequency features observed in force signals, this paper proposes a non-visual classifier, <bold xm
Online Handwritten Text Recognition (OLHTR) has gained considerable attention for its diverse range of applications. Current approaches usually treat OLHTR as a sequence recognition task, employing either a single trajectory or image encoder, or multi-stream encoders, combined with a CTC or attentio…
Talking head generation intends to produce vivid and realistic talking head videos from a single portrait and speech audio clip. Although significant progress has been made in diffusion-based talking head generation, almost all methods rely on autoregressive strategies, which suffer from limited con…
Event-Based cameras offer significant advantages due to their high temporal resolution and low power consumption. However, when deploying multiple such cameras, a critical challenge emerges: each camera operates on an independent time system, resulting in temporal misalignment that severely degrades
This research tackles the challenge of real-time active view selection and uncertainty quantification on visual quality for active 3D reconstruction. Visual quality is a critical aspect of 3D reconstruction. Recent advancements such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) h…
Although fully end-to-end speaker diarization systems have made significant progress in recent years, modular systems often achieve superior results in real-world scenarios due to their greater adaptability and robustness. Historically, modular speaker diarization methods have seldom discussed how t…
This work proposes the Lattice Boltzmann Model (LBM) to learn real-world pixel dynamicity for visual tracking. LBM decomposes visual representations into dynamic pixel lattices and solves pixel motion states through collision-streaming processes. Specifically, the high-dimensional distribution of t…
Inertial tracking is vital for autonomous robots and has gained popularity with the ubiquity of low-cost Inertial Measurement Units (IMUs) and deep learning-powered tracking algorithms. Existing works, however, have not fully utilized IMU measurements, particularly magnetometers, nor maximized the p
This paper presents a mixed traffic control policy designed to optimize traffic efficiency across diverse road topologies, addressing issues of congestion prevalent in urban environments. A model-free reinforcement learning (RL) approach is developed to manage large-scale traffic flow, using data co
Granular materials (GMs) are ubiquitous in daily life. Understanding their properties is also important, especially in agriculture and industry. However, existing works require dedicated measurement equipment and also need large human efforts to handle a large number of particles. In this paper, we
Deep learning (DL)-based speaker diarization methods have proven powerful performance comparing to traditional clustering-based methods for multi-talker speech diarization and recognition in farfield scenes. However, most DL-based approaches cannot utilize the spatial information well due to the poo…
Domain adaptation is an inspiring solution to the misalignment issue of day/night image features for nighttime UAV tracking. However, the one-step adaptation paradigm is inadequate in addressing the prevalent difficulties posed by low-resolution (LR) objects when viewed from the UAVs at night, owing…
Fabrics present significant challenges to robotic manipulation due to their complex dynamics and infinite degrees of freedom. This letter proposes a non-prehensile approach to aligning a fabric cut piece to a specified target pose, which is a common step for many garment manufacturing tasks. Compare
Robots, constrained by limited onboard computing resources, often encounter situations wherein high-resolution and high-bit-rate videos captured by their cameras necessitate compression before further analysis. In this paper, we propose a novel video semantic segmentation paradigm for compressed vid…
Machine learning models provide alternatives for efficiently recognizing complex patterns from data, but the main concern in applying them to modeling physical systems stems from their physics-agnostic design, leading to learning methods that lack interpretability, robustness, and data efficiency. T…
In multi-speaker scenarios, automatic speech recognition (ASR) models rely on pre-processed audio after speaker separation. However, when the target speaker is not accurately separated, ASR models face limitations in reaching their peak performance. To address this issue, we propose a speaker-adapti…
Microscopic traffic simulation plays a crucial role in transportation engineering by providing insights into individual vehicle behavior and overall traffic flow. However creating a realistic simulator that accurately replicates human driving behaviors in various traffic conditions presents signific…
Enabling robotic agents to perform complex long-horizon tasks has been a long-standing goal in robotics and artificial intelligence (AI). Despite the potential shown by large language models (LLMs), their planning capabilities remain limited to short-horizon tasks and they are unable to replace the…
Ultra-high resolution image segmentation poses a formidable challenge for UAVs with limited computation resources. Moreover, with multiple deployed tasks (e.g., mapping, localization, and decision making), the demand for a memory efficient model becomes more urgent. This letter delves into the intri
Traffic congestion is a persistent problem in our society. Previous methods for traffic control have proven futile in alleviating current congestion levels leading researchers to explore ideas with robot vehicles given the increased emergence of vehicles with different levels of autonomy on our road…
"Recently, Handwritten Mathematical Expression Recognition (HMER) has gained considerable attention in pattern recognition for its diverse applications in document understanding. Current methods typically approach HMER as an image-to-sequence generation task within an autoregressive (AR) encoder-dec…
In this letter, we present a novel approach for planning an object's Next Best Views (NBV) so that a depth camera can collect the object's surface point cloud and reconstruct its 3D model with a small number of consequent views. Our focus is especially on thin and curved metal plates, and we use a r
The complex dynamicity of open-world objects presents non-negligible challenges for multi-object tracking (MOT) often manifested as severe deformations fast motion and occlusions. Most methods that solely depend on coarse-grained object cues such as boxes and the overall appearance of the object are…
Visual object tracking has significantly promoted autonomous applications for unmanned aerial vehicles (UAVs). However, learning robust object representations for UAV tracking is especially challenging in complex dynamic environments, when confronted with aspect ratio change and occlusion. These cha…
Nighttime UAV tracking under low-illuminated scenarios has achieved great progress by domain adaptation (DA). However, previous DA training-based works are deficient in narrowing the discrepancy of temporal contexts for UAV trackers. To address the issue, this work proposes a prompt-driven temporal…
The escalating demands for real-time scene comprehension in modern industries underscore the growing significance of semantic information in the daily tasks of robots, particularly in areas like autonomous inspection and target searching. This letter introduces a semantics-aware receding horizon pla
Previous Multimodal Information based Speech Processing (MISP) challenges mainly focused on audio-visual speech recognition (AVSR) with commendable success. However, the most advanced back-end recognition systems often hit performance limits due to the complex acoustic environments. This has prompte…
We study four data augmentation (DA) techniques and two model architectures on realistic data for sound event localization and detection (SELD). First, based on ResNet-Conformer (RC), we compare the four DA approaches on the realistic DCASE 2022 SELD test set which is often not easy to handle due to…
Imitation learning holds great promise for addressing the complex task of autonomous urban driving, as experienced human drivers can navigate highly challenging scenarios with ease. While behavior cloning is a widely used imitation learning approach in autonomous driving due to its exemption from ri…
The automation of unmanned aerial vehicles (UAVs) has been greatly promoted by visual object tracking methods with onboard cameras. However, the random and complicated real noise produced by the cameras seriously hinders the performance of state-of-the-art (SOTA) UAV trackers, especially in low-illu
Motion deblurring is a critical ill-posed problem that is important in many vision-based robotics applications. The recently proposed event-based double integral (EDI) provides a theoretical framework for solving the deblurring prob-lem with the event camera and generating clear images at high frame…
Understanding dynamic hand motions and actions from egocentric RGB videos is a fundamental yet challenging task due to self-occlusion and ambiguity. To address occlusion and ambiguity, we develop a transformer-based framework to exploit temporal information for robust estimation. Noticing the differ…
Video information has been widely introduced to speech enhancement as its contribution at low signal-to-noise ratios (SNRs). Conventional audio-visual speech enhancement networks take noisy speech and video as input and learn features of clean speech directly. To reduce the large SNR gap between the…
This study focuses on the design of a loss function for a deep neural network (DNN)-based model with two branches, which is used to solve sound event localization and detection (SELD) on low-resource realistic data. To this end, we employ a secondary network for audio classification, which provides…
In robotic insertion tasks where the uncertainty exceeds the allowable tolerance, a good search strategy is essential for successful insertion and significantly influences efficiency. The commonly used blind search method is time-consuming and does not exploit the rich contact information. In this p…
Human skin can accurately sense the self-decoupled normal and shear forces when in contact with objects of different sizes. Although there exist many soft and conformable tactile sensors on robotic applications able to decouple the normal force and shear forces, the impact of the size of object in c…
Autonomous motion planning is challenging in multi-obstacle environments due to nonconvex collision avoidance constraints. Directly applying numerical solvers to these nonconvex formulations fails to exploit the constraint structures, resulting in excessive computation time. In this letter, we prese
Transducer is one of the mainstream frameworks for streaming speech recognition. There is a performance gap between the streaming and non-streaming transducer models due to limited context. To reduce this gap, an effective way is to ensure that their hidden and output distributions are consistent, w…
In this work, we present a novel method, named AV2vec, for learning audio-visual speech representations by multimodal self-distillation. AV2vec has a student and a teacher module, in which the student performs a masked latent feature regression task using the multimodal target features generated onl…
The Multimodal Information based Speech Processing (MISP) 2022 challenge aimed to enhance speech processing performance in harsh acoustic environments by leveraging additional modalities such as video or text. The challenge included two tracks: audio-visual speaker diarization (AVSD) and audio-visua…
In this letter, we propose a novel framework for tactile-based dexterous manipulation learning with a blind anthropomorphic robotic hand, i.e. without visual sensing. First, object-related states were extracted from the raw tactile signals by a graph-based perception model - TacGNN. The resulting ta
Calibrating the extrinsic parameters of sensory devices is crucial for fusing multi-modal data. Recently, event cameras have emerged as a promising type of neuromorphic sensors, with many potential applications in fields such as mobile robotics and autonomous driving. When combined with LiDAR, they
The Multi-modal Information based Speech Processing (MISP) challenge aims to extend the application of signal processing technology in specific scenarios by promoting the research into wake-up words, speaker diarization, speech recognition, and other technologies. The MISP2022 challenge has two trac…
Operating unmanned aerial vehicles (UAVs) in complex environments that feature dynamic obstacles and external disturbances poses significant challenges, primarily due to the inherent uncertainty in such scenarios. Additionally, inaccurate robot localization and modeling errors further exacerbate the…
We study six-dimensional (6D) perceptive peg-in-hole problem for practical connector insertion task in this paper. To enable the manipulator system to handle different types of pegs in complex environment, we develop a perceptive robotic assembly system that utilizes an in-hand RGB-D camera for peg-…
Large-scale vision-language pretrained (VLP) models like CLIP have shown remarkable performance on various downstream cross-modal tasks. However, they are usually biased towards English due to the lack of sufficient non-English image-text pairs. Existing multilingual VLP methods often learn retrieva…
In this letter, we introduce a generalized continuous collision detection (CCD) framework for the mobile robot along the polynomial trajectory in cluttered environments including various static obstacle models. Specifically, we find that the collision conditions between robots and obstacles could be
Autonomous exploration is a fundamental task for mobile robots. However, a major limitation is that in practical applications, robots usually face dynamic obstacles such as pedestrian crowds. These dynamic objects create significant challenges for both collision-free navigation and accurate localiza
This letter presents an uncertainty-aware motion planner for an autonomous underwater vehicle (AUV) which navigates in a cluttered scenario under an uncertain flow field. Safe and efficient navigation in such environments requires a precise motion model of an AUV. However, the motion of an AUV is di
In this letter, we introduce acentralized planner with low computational cost to schedule the motions of multiple Automated Guided Vehicles (AGVs) at the intersection of pre-defined polynomial curves. In particular, we find that the collision conditions between two AGVs along polynomial paths can be
Focus control (FC) is crucial for cameras to capture sharp images in challenging real-world scenarios. The autofocus (AF) facilitates the FC by automatically adjusting the focus settings. However, due to the lack of effective AF methods for the recently introduced event cameras, their FC still relie…
In this letter, we present a distributed approach to automatically collect a flock with a group of robots. This collecting problem is challenging due to the fact that the flock size is usually much greater than that of robots, meanwhile the local sensing range of robots may not ensure a globally suc
3D LiDAR sensors can provide 3D point clouds of the environment, and are widely used in automobile navigation; while 2D LiDAR sensors can only provide point cloud in a 2D sweeping plane, and then are only used for navigating robots of small height, e.g., floor mopping robots. In this letter, we prop
Dynamic state representation learning is essential for robot learning. Good latent space that can accurately describe dynamic transition and constraints can significantly accelerate reinforcement learning training as well as reduce motion planning complexity. However, deformable object have very com
Emergence of massive dynamic objects will diversify spatial structures when robots navigate in urban environments. Therefore, the online removal of dynamic objects is critical. In this paper, we introduce a novel online removal framework for highly dynamic urban environments. The framework consists…
Autonomous exploration in scenes with moving pedestrians is critical for deploying autonomous mobile robots in populated places such as malls, airports, and museums. The existence of dynamic obstacles poses challenges on achieving an efficient, safe, and robust exploration system: the robot may get
In this paper, we aim to forecast a future trajectory distribution of a moving agent in the real world, given the social scene images and historical trajectories. Yet, it is a challenging task because the ground-truth distribution is unknown and unobservable, while only one of its samples can be app…
This paper presents a Generative prior ReciprocAted Invertible rescaling Network (GRAIN) for generating faithful high-resolution (HR) images from low-resolution (LR) invertible images with an extreme upscaling factor (64x). Previous researches have leveraged the prior knowledge of a pretrained GAN m…
We present a novel high-resolution face swapping method using the inherent prior knowledge of a pre-trained GAN model. Although previous research can leverage generative priors to produce high-resolution results, their quality can suffer from the entangled semantics of the latent space. We explicitl…
Understanding human intentions during interactions has been a long-lasting theme, that has applications in human-robot interaction, virtual reality and surveillance. In this study, we focus on full-body human interactions with large-sized daily objects and aim to predict the future states of objects
Learning communication via deep reinforcement learning (RL) or imitation learning (IL) has recently been shown to be an effective way to solve Multi-Agent Path Finding (MAPF). However, existing communication based MAPF solvers focus on broadcast communication, where an agent broadcasts its message t
Deep learning models are able to approximate one specific dynamical system but struggle at learning generalisable dynamics, where dynamical systems obey the same laws of physics but contain different numbers of elements (e.g., double- and triple-pendulum systems). To relieve this issue, we proposed…
The challenges to solving the collision avoidance problem lie in adaptively choosing optimal robot velocities in complex scenarios full of interactive obstacles. In this letter, we propose a distributed approach for multi-robot navigation which combines the concept of reciprocal velocity obstacle (R
Although the manipulating of the unmanned aerial manipulator (UAM) has been widely studied, vision-based UAM approaching, which is crucial to the subsequent manipulating, generally lacks effective design. The key to the visual UAM approaching lies in object tracking, while current UAM tracking typic…
Tactile sensors of high spatial resolution can provide rich contact information in terms of accurate contact location and force magnitude for robots. However, achieving a high spatial resolution normally requires a high density of tactile sensing cells (or taxels), which will inevitably lead to crow
In this paper we discuss the rational of the Multi-model Information based Speech Processing (MISP) Challenge, and provide a detailed description of the data recorded, the two evaluation tasks and the corresponding baselines, followed by a summary of submitted systems and evaluation results. The MIS…
This paper demonstrates that multilingual pretraining and multilingual fine-tuning are both critical for facilitating cross-lingual transfer in zero-shot translation, where the neural machine translation (NMT) model is tested on source languages unseen during supervised training. Following this idea…
We propose a deep visuo-tactile model for real-time estimation of the liquid inside a deformable container in a proprioceptive way. We fuse two sensory modalities, i.e., the raw visual inputs from the RGB camera and the tactile cues from our specific tactile sensor without any extra sensor calibrati…
Robot hand is essential for a fully functional robot and designing a good robot hand is a sophisticated job that challenges the designer’s knowledge and experience. This paper presents a computational framework for automatic optimal robot hand design based on reinforcement learning (RL), which consi…
Safety and efficiency are two crucial factors for human-robot collaboration. It is challenging to ensure human safety while not sacrificing the task efficiency. In this letter, we present a reinforcement learning (RL) based method with a hazard estimator to balance these two factors. Our method has
Planar mechanisms dominate modern designs of legged robots with remote actuator placement for robust agility in ground mobility. This paper presents a novel design of robotic leg modules using the Bennett linkage, driven by two coaxially arranged quasi-direct actuators capable of omnidirectional gro…
Convolution has been used for fast computation of reachability maps, but it has high computational costs when performing SE(3) convolution operations for general joint arrangements in industrial robots and 3D workspace. Its application is also limited to planar robots, 2D workspace, or robots with s…
This paper studies a novel encirclement guaranteed cooperative pursuit problem involving N pursuers and a single evader in an unbounded two-dimensional game domain. Throughout the game, the pursuers are required to maintain encirclement of the evader, i.e., the evader should always stay inside the c…
Robot-assisted minimally invasive surgery (RMIS) has become increasingly popular in the resection of cancers. However, the lack of tactile feedback in clinical RMIS limits the surgeon's haptic understanding of tissue mechanics, making it hard to detect tissue abnormalities (e.g., tumor) efficiently.
This letter presents a novel design of a soft tactile finger with omni-directional adaptation using multi-channel optical fibers for rigid-soft interactive grasping. Machine learning methods are used to train a model for real-time prediction of force, torque, and contact using the tactile data colle
Living objects are hard to grasp because they can actively dodge and struggle by writhing or deforming while or even prior to being contacted and modeling or predicting their responses to grasping is extremely difficult. This letter presents an algorithm based on reinforcement learning (RL) to attac
In thisletter, we present a novel optimization-based framework for autonomous excavator trajectory generation under task-specific constraints. Traditional excavation trajectory generators over-simplify the geometric trajectory parameterization thereby limiting the space for optimization. To expand t
HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to the deployed expensive sensors and time-consuming computation. Camera-based methods usually need to separately perform road segmentation and view transformation, which often causes distortion and the abse…
As one of the most popular multi-agent path planning approaches, the optimal reciprocal collision avoidance (ORCA) algorithm assumes that each agent takes half the responsibility for collision avoidance. However, due to the asymmetric situation faced by adjacent agents, they are expected to take dif
Previous work mainly focuses on improving cross-lingual transfer for NLU tasks with a multilingual pretrained encoder (MPE), or improving the performance on supervised machine translation with BERT. However, it is under-explored that whether the MPE can help to facilitate the cross-lingual transfera…
Having the merits of chemical inertness and immunity to electromagnetic interference, light weight, small size, and softness, optical waveguides have attracted much attention in making tactile sensors recently. This paper presents a new design of waveguide using two layers of cores, one of which has…
Unlike autonomous ground vehicles (AGVs), unmanned aerial vehicles (UAVs) have a higher dimensional configuration space, which makes the motion planning of multi-UAVs a challenging task. In addition, uncertainties and noises are more significant in UAV scenarios, which increases the difficulty of au
In human-robot collaboration, it is crucial for the robot to make its intentions clear and predictable to the human partners. Inspired by the mutual learning and adaptation of human partners, we suggest an actor-critic approach for a legible robot motion planner. This approach includes two neural ne…
The outstanding computational efficiency of discriminative correlation filter (DCF) fades away with various complicated improvements. Previous appearances are also gradually forgotten due to the exponential decay of historical views in traditional appearance updating scheme of DCF framework, reducin…
Motion planning for robots of high degrees-of-freedom (DOFs) is an important problem in robotics with sampling-based methods in configuration space \mathcal{C}\mathcal{C} as one popular solution. Recently, machine learning methods have been introduced into sampling-based motion planning methods, whi…
We present a novel algorithm (DeepMNavigate) for global multi-agent navigation in dense scenarios using deep reinforcement learning (DRL). Our approach uses local and global information for each robot from motion information maps. We use a three-layer CNN that takes these maps as input to generate a…
The spectral information of acoustic scenes is diverse and complex, which poses challenges for acoustic scene tasks. To improve the classification performance, a variety of convolutional neural networks (CNNs) are proposed to extract richer semantic information of scene utterances. However, the diff…
Correlation filter-based tracking has been widely applied in unmanned aerial vehicle (UAV) with high efficiency. However, it has two imperfections, i.e., boundary effect and filter corruption. Several methods enlarging the search area can mitigate boundary effect, yet introducing undesired backgroun…
Deep reinforcement learning has great potential to acquire complex, adaptive behaviors for autonomous agents automatically. However, the underlying neural network polices have not been widely deployed in real-world applications, especially in these safety-critical tasks (e.g., autonomous driving). O…
Unmanned aerial vehicles (UAVs) are frequently used for large-scale scene mapping and reconstruction. However, in most cases, drones are operated manually, which should be more effective and intelligent. In this article, we present a method of real-time UAV path planning for autonomous urban scene r…
Robot learning is widely accepted by academia and industry with its potentials to transform autonomous robot control through machine learning. Inspired by widely used soft fingers on grasping, we propose a method of rigid-soft interactive learning, aiming at reducing the time of data collection. In
Due to its high dimensionality, deformable object manipulation is a challenging problem in robotics. In this letter, we present a deep neural network based controller to servo control the position and shape of deformable objects with unknown deformation properties. In particular, a multi-layer neura
In this paper, we design a two-stage single-channel speaker-dependent speech separation approach for the CHiME-5 Challenge, targeting the problem of far-field and multi-talker conversational speech recognition in dinner party scenarios involving background noises, reverberations and overlapping spee…
We present a novel approach for manipulating high-DOE deformable objects such as cloth. Our approach uses a random-forest-based controller that maps the observed visual features of the cloth to an optimal control action of the manipulator. The topological structure of this random-forest is determine
In this paper, we propose a novel compact reachability map representation for excavator motion planning. The constructed reachability map can concisely encode the bucket’s reachable pose and the translation capability limited by excavator’s kinematic structure. By explicitly exploiting the property…
Curvilinear structures are frequently observed in various images in different forms, such as blood vessels or neuronal boundaries in biomedical images. In this paper, we propose a novel curvilinear structure segmentation approach using context-aware spatio-recurrent networks. Instead of directly seg…
A deep understanding of the movement mechanism of the human fingers is essential for achieving a human-like actuation mechanism for anthropomorphic robotic hands. After carefully analyzing a set of anatomical theories of human fingers, we propose a novel and more reasonable actuation mechanism of th
Our goal is to navigate a mobile robot to navigate through environments with dense crowds, e.g., shopping malls, canteens, train stations, or airport terminals. In these challenging environments, existing approaches suffer from two common problems: the robot may get frozen and cannot make any progre
Manual plant phenotyping is slow, error prone, and labor intensive. In this letter, we present an automated robotic system for fast, precise, and noninvasive measurements using a new deep-learning-based next-best view planning pipeline. Specifically, we first use a deep neural network to estimate a
In this letter, we present a robotic navigation algorithm with natural language interfaces that enables a robot to safely walk through a changing environment with moving persons by following human instructions such as “go to the restaurant and keep away from people.” We first classify human instruct
In this paper, we propose a novel iterative multi-task framework to complete the segmentation mask of an occluded vehicle and recover the appearance of its invisible parts. In particular, firstly, to improve the quality of the segmentation completion, we present two coupled discriminators that intro…
Combining deep neural networks with reinforcement learning has shown great potential in the next-generation intelligent control. However, there are challenges in terms of safety and cost in practical applications. In this pa- per, we propose the Intervention Aided Reinforcement Learning (IARL) frame
The complex physical properties of highly deformable materials such as clothes pose significant challenges for autonomous robotic manipulation systems. We present a novel visual feedback dictionary-based method for manipulating deformable objects towards a desired configuration. Our approach is base…
In this letter, we present a general approach to automatically visual servo control the position and shape of a deformable object whose deformation parameters are unknown. The servo control is achieved by online learning a model mapping between the robotic end-effector's movement and the object's de
Developing a safe and efficient collision avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generates its paths without observing other robots' states and intents. While other distributed multi-robot collision avoidance systems exist, they often requ…
This letter proposes a unified GPU-parallelizable approach for robot forward dynamics (FD) computation based on the key fact that parallelism of prevailing FD algorithms benefits from the essential band sparsity of the joint space inertia (JSI) matrix or its inverse. The existing FD algorithms are c
High-speed, low-latency obstacle avoidance that is insensitive to sensor noise is essential for enabling multiple decentralized robots to function reliably in cluttered and dynamic environments. While other distributed multiagent collision avoidance systems exist, these systems require online geomet
We present a novel approach for collision-free global navigation for continuous-time multi-agent systems with general linear dynamics. Our approach is general and can be used to perform collision-free navigation in 2D and 3D workspaces with narrow passages and crowded regions. As part of pre-computa…
A new parallel framework for fast computation of inverse and forward dynamics of articulated robots based on prefix sums (scans) is proposed. We first re-investigate the well-known recursive Newton-Euler formulation for robot dynamics and show that the forward-backward propagation process for robot
Pick-and-place regrasp extends the manipulation capability of a robot by using a sequence of regrasps to accomplish tasks that are not possible using a single grasp due to constraints such as kinematics or collisions between the robot and the environment. Previous work on pick-and-place only leverag…
Pick-and-place regrasp is an important manipulation skill for a robot. It helps a robot accomplish tasks that cannot be achieved within a single grasp, due to constraints such as kinematics or collisions between the robot and the environment. Previous work on pick-and-place regrasp only leveraged fl
We present a novel method to compute the approximate global penetration depth (PD) between two nonconvex geometric models. Our approach consists of two phases: offline precomputation and run-time queries. In the first phase, our formulation uses a novel sampling algorithm to precompute an approximat
We present a decentralized algorithm for group-based coherent and reciprocal multi-agent navigation. In addition to generating collision-free trajectories for each agent, our approach is able to simulate macroscopic group movements and proxemic behaviors that result in coherent navigation. Our appro…
We present a novel method for caging grasps in this paper by stretching ropes on the surface of a 3D object. Both topology and shape of a model to be grasped has been considered in our approach. Our algorithm can guarantee generating local minimal rings on every topological branches of a given model
Manipulation of deformable objects is a widely applicable but challenging task in robotics. One promising nonparametric approach for this problem is trajectory transfer, in which a non-rigid registration is computed between the starting scene of the demonstration and the scene at test time. This reg…
Reliable manipulation of everyday household objects is essential to the success of service robots. In order to accurately manipulate these objects, robots need to know objects' full 6-DOF pose, which is challenging due to sensor noise, clutters and occlusions. In this paper, we present a new approac…