46 accepted papers
Autonomous docking between Unmanned Aerial Vehicles (UAVs) and ground robots is essential for heterogeneous systems, yet most existing approaches target wheeled platforms whose limited mobility constrains exploration in complex terrains. Quadruped robots offer superior adaptability but undergo frequ…
Building scalable and reusable multi-agent decision policies from offline datasets remains a challenge in offline multi-agent reinforcement learning (MARL), as existing methods often rely on fixed observation formats and action spaces that limit generalization. In contrast, large language models (LL…
Cutting-edge robot learning techniques including foundation models and imitation learning from humans all pose huge demands on large-scale and high-quality datasets which constitute one of the bottleneck in the general intelligent robot fields. This paper presents the Kaiwu multimodal dataset to add…
This paper presents a closed-loop automation framework for heterogeneous modular robots, encompassing the entire pipeline from morphological construction to adaptive control. Within this framework, a mobile manipulator manipulates heterogeneous functional modules—including structural, joint, and whe…
Pneumatic soft robots are well-suited for minimally invasive surgery owing to their compliance and safe interaction with tissues. However, achieving highly adaptive control is difficult owing to modeling inaccuracies, inter-chamber coupling, and disturbances from surgical instruments. Non-learning a…
Box/cabinet scenarios pose with stacked objects significant challenges for robotic motion due to visual occlusions and constrained free space. Traditional collision-free trajectory planning methods often fail when no collision-free paths exist, and may even lead to catastrophic collisions caused by …
Estimation of robotic dynamic states in physical human-robot interaction (pHRI) is crucial for robots to handle real-world uncertainties. Since wearable tactile sensor arrays have been increasingly applied in robots, the tactile sensory outcomes (TSOs) during pHRIs would provide practical and ideal
Cooperative tasks are common in multi-agent systems, with closely cooperative tasks being a special case of this, where a change in the state of the environment requires multiple agents to perform a specific operation at the same time. Take a box-pushing task as an example, the box is heavy and requ…
In the context of imitation learning, visuomotor-based diffusion policy learning is one of the main directions in robotic manipulation. Most of these approaches rely on point clouds as observation inputs and construct scene representations through point clouds feature learning, which enables them to…
Unmanned aerial–aquatic vehicles (UAAVs) provide cross-domain adaptability and broad visions, while autonomous underwater vehicles (AUVs) support long-duration operations. This work integrates the two by developing a rapid underwater docking and releasing system. An autonomous clamping mechanism is …
Due to control limitations in the denoising process and the lack of training, zero-shot video editing methods often struggle to meet user instructions, resulting in generated videos that are visually unappealing and fail to fully satisfy expectations. To address this problem, we propose Align-A-Vide…
Multi-agent systems must learn to communicate and understand interactions between agents to achieve cooperative goals in partially observed tasks. However, existing approaches lack a dynamic directed communication mechanism and rely on global states, thus diminishing the role of communication in cen…
Dual-arm manipulation of fabrics is important and complex in the field of embodied intelligence for robots. Previous work has mostly focused on extracting the geometric features of fabrics, but has neglected the knowledge and experience constraints on grasp postures like humans, which greatly affect
3D part assembly is a promising task in 3D computer vision and robotics, focusing on assembling 3D parts together by predicting their 6-DoF poses. Like most 3D shape understanding tasks, existing methods primarily address this task by memorizing the poses of parts during the training process, leadin…
Keyframe insertion is critical for the performance and robustness of SLAM systems. However, traditional heuristic-based methods often lead to suboptimal keyframe selection, compromising the accuracy of localization and mapping. To address this, we propose KINND, a lightweight neural network-based fr
Garment manipulation is a significant challenge for robots due to the complex dynamics and potential self-occlusion of garments. Most existing methods of efficient garment unfolding overlook the crucial role of standardization of flattened garments, which could significantly simplify downstream task…
Reinforcement Learning from Human Feedback (RLHF) enables robots to learn cooperative strategies aligned with human expectations by incorporating feedback into the learning process. However, existing RLHF methods rely on explicit query-based feedback, which is limited for complex, long-horizon tasks
Point cloud classification is critical for three-dimensional scene understanding. However, in real-world scenarios, depth cameras often capture partial, single-view point clouds of objects with different poses, making their accurate classification a challenge. In this paper, we propose a novel point
Multi-robot cooperative tracking, as a vital sub-field of multi-robot collaboration, exhibits significant potential in areas such as military reconnaissance and emergency rescue. Conventional dynamic object tracking methods often face issues of incomplete target detection and even loss in complex sc
Cooperative tasks are common in multi-agent systems, with closely cooperative tasks being a special case of this, where a change in the state of the environment requires multiple agents to perform a specific operation at the same time. Take a box-pushing task as an example, the box is heavy and requ
Multi-modal fusion perception enhances robotic performance in complex tasks by providing more comprehensive information than single modality. While tactile and proprioceptive sensing are effective for direct contact tasks like grasping, current research mainly focuses on vision-language fusion, negl
Multi-modal perception plays a crucial role in preventing deformation and damage during the robotic manipulation of deformable objects. However, integrating new heterogeneous modalities into existing robotic perception frameworks remains a significant challenge, primarily due to the need for massive
"Advances in 3D generation have facilitated sequential 3D model generation (a.k.a 4D generation), yet its application for animatable objects with large motion remains scarce. Our work proposes AnimatableDreamer, a text-to-4D generation framework capable of generating diverse categories of non-rigid…
Collaborative tasks are important in multi-agent systems. Multi-agent reinforcement learning is a commonly used technique for solving multi-agent cooperative policy learning. The closely collaborative task is a special but common case within cooperative tasks, where the change in the environmental s
Embodied visual navigation has witnessed significant advancements. However, most studies commonly assume that environments are static and contain at least one collision-free path. In human environments, agents frequently encounter challenges when navigating through scenes with disarranged objects. I
Collaboration between humans and robots is becoming increasingly crucial in our daily life. In order to accomplish efficient cooperation, trust recognition is vital, empowering robots to predict human behaviors and make trust-aware decisions. Consequently, there is an urgent need for a generalized a…
In-flight high-speed object capturing is crucial in nature to improve survival and adaptation to the environment, such as the predation of frogs, leopards, and eagles. Despite its ubiquitousness in nature, capturing fast-moving objects is extremely challenging in engineering implementations. In this…
Recently, tactile sensing has attracted great interests in robotics, especially for exploring unstructured objects. Sensor arrays play an important role in the exploration, which generates rich spatio-temporal information. In this work, we propose an efficient tactile recognition model, X-Tacformer.…
Automatic assembly is a promising research topic in 3D computer vision and robotics. Existing works focus on generating assembly (e.g., IKEA furniture) from scratch with a set of parts, namely 3D part assembly. In practice, there are higher demands for the robot to take over and finish an incomplete…
In this paper, we propose a continuous-time-based LiDAR-inertial-vehicle odometry method, which can tightly fuse the data from Light Detection And Ranging (LiDAR), inertial measurement units (IMU), and vehicle measurements. The lateral acceleration constraint is further added to trajectory estimatio…
Movement Primitive (MP) is a promising Learning from Demonstration (LfD) framework, which is commonly used to learn movements from human demonstrations and adapt the learned movements to new task scenes. A major goal of MP research is to improve the adaptability of MP to various target positions and
Goal-Conditioned Reinforcement Learning (GCRL) can enable agents to spontaneously set diverse goals to learn a set of skills. Despite the excellent works proposed in various fields, reaching distant goals in temporally extended tasks remains a challenge for GCRL. Current works tackled this problem b
With the advent of solid-state LiDAR, a series of related studies have boosted the development of Simultaneous Localization and Mapping (SLAM). However, existing methods cannot work well in indoor environments. In the letter, the reflectivity measurement of the solid-state LiDAR is exploited to impr
The task of zero-shot object goal visual navigation (ZSON) aims to enable robots to locate previously “unseen” objects by visual observations. This task presents a significant challenge since the robot must transfer the navigation policy learned from “seen” objects to “unseen” objects through auxili
Traditional Visual Odometry (VO) methods that utilize visible cameras frequently degrade in challenging illumination environments. Alternative vision sensors such as thermal cameras are promising for all-day navigation since the delivered thermal images are invariant to ambient illumination. However
Object goal visual navigation is a challenging task that aims to guide a robot to find the target object based on its visual observation, and the target is limited to the classes pre-defined in the training stage. However, in real households, there may exist numerous target classes that the robot ne…
Meta-learning-based few-shot detectors use one K-average-pooled prototype (averaging along K-shot dimension) in both Region Proposal Network (RPN) and Detection head (DH) for query detection. Such plain operation would harm the FSOD performance in two aspects: 1) the poor quality of the prototype, a…
Learning to achieve a user-specified objective from a random position in unseen environments is challenging for image-guided navigation agents. The abilities of long-horizon reasoning and semantic understanding are still lacking. Inspired by the human memory mechanism, we introduce a neural multi-st
Complex question answering over knowledge base (Complex KBQA) is challenging because it requires various compositional reasoning capabilities, such as multi-hop inference, attribute comparison, set operation, etc. Existing benchmarks have some shortcomings that limit the development of Complex KBQA:…
Goal-conditioned reinforcement learning is a crucial yet challenging algorithm which enables agents to achieve multiple user-specified goals when learning a set of skills in a dynamic environment. However, it typically requires millions of the environmental interactions explored by agents, which is
Early exit mechanism aims to accelerate the inference speed of large-scale pre-trained language models. The essential idea is to exit early without passing through all the inference layers at the inference stage. To make accurate predictions for downstream tasks, the hierarchical linguistic informat…
Recent studies have revealed a security threat to natural language processing (NLP) models, called the Backdoor Attack. Victim models can maintain competitive performance on clean samples while behaving abnormally on samples with a specific trigger word inserted. Previous backdoor attacking methods…
Reasoning over commonsense knowledge bases (CSKB) whose elements are in the form of free-text is an important yet hard task in NLP. While CSKB completion only fills the missing links within the domain of the CSKB, CSKB population is alternatively proposed with the goal of reasoning unseen assertions…
Side effects during neural network tuning are typically measured by overall accuracy changes. However, we find that even with similar overall accuracy, existing tuning methods result in non-negligible instance-wise side effects. Motivated by neuroscientific evidence and theoretical results, we demon…
Frequency aliasing in the digital capture of display screens leads to the moir´e pattern, appearing as stripe-shaped distortions in images. Efforts to demoir´eing have been made recently in a learning fashion due to the complexity and diversity of the pattern appearance. However, existing methods ca…
Moire pattern is a common image quality degradation caused by frequency aliasing between monitors and cameras when taking screen-shot photos. The complex frequency distribution, imbalanced magnitude in colour channels, and diverse appearance attributes of moire pattern make its removal a challenging…