93 accepted papers
Tactile and proximity sensing is fundamental for achieving autonomous robotic manipulation and safe human-robot interaction. However, traditional dual-mode sensors often face challenges such as environmental interference and the perception gap between far-field vision and near-field contact. This st…
Visual simultaneous localization and mapping (VSLAM) is a foundational technology in robotics, providing an optimal balance of cost and accuracy. However, existing systems often lack robustness in environments with fast motion, dynamic lighting, or low texture. This letter introduces ML-SLAM, a hybr
In this work, we present CollabVLA, a self-reflective vision-language-action framework that transforms a standard visuomotor policy into a collaborative assistant. CollabVLA tackles key limitations of prior VLAs, including domain overfitting, non-interpretable reasoning, and the high latency of auxi…
The competition among a swarm often brings about astonishing potential beyond the ordinary. This paper proposes an approach for enhancing autonomous obstacle avoidance in aerial robots through competitive training. By constructing a multi-agent environment, we introduce competition between individua
This paper investigates training better visual world models for robot manipulation, i.e., models that can predict future visual observations by conditioning on past frames and robot actions. Specifically, we consider world models that operate on RGB-D frames (RGB-D world models). As opposed to canon
This paper investigates training better visual world models for robot manipulation, i.e., models that can predict future visual observations by conditioning on past frames and robot actions. Specifically, we consider world models that operate on RGB-D frames (RGB-D world models). As opposed to canon…
Operating household appliances by reading and understanding user manuals remains a fundamental and challenging problem in robotics. Recent works leverage large language models (LLMs) and vision-language models (VLMs) to interpret manuals, improving appliance operation success. However, these approac…
Open-World Object Detection (OWOD) presents a critical challenge for modern computer vision systems: detecting known classes, identifying unknown objects, and incrementally learning to recognize them over time. However, current approaches have two fundamental limitations: (1) the fixed-dimensional c…
Standard on-policy reinforcement learning relies on heuristic clipping to enforce trust regions, but this mechanism imposes a severe cost by indiscriminately truncating high-return yet high-divergence updates. We demonstrate that explicitly constraining the *policy ratio **variance*** provides a pri…
Integrating Vision-Language Models (VLMs) into robotics has facilitated the development of generalizable Vision-Language Action (VLA) policies. However, unified discrete frameworks lag behind decoupled continuous designs due to limitations in action chunking and temporal modeling. To address this, w…
A foundational humanoid motion tracker is expected to be able to track diverse, highly dynamic, and contact-rich motions. More importantly, it needs to operate stably in real-world scenarios against various dynamics disturbances including terrains, external forces, and physical property changes for …
Terrain classification is crucial for robotic navigation especially in unknown environment. Existing terrain classification methods usually have high requirements for environment conditions and robot motions, making them challenging to apply to real-world scenarios. In this paper, we develop a novel
Electrical capacitance tomography (ECT) is a contactless and non-invasive imaging technique, which visualizes the internal permittivity distribution around a region utilizing boundary capacitance measurements. It has been widely used in the fields of object classification, tactile sensing and multip
Current service robots suffer from limited natural language communication abilities, heavy reliance on predefined commands, ongoing human intervention, and, most notably, a lack of proactive collaboration awareness in human-populated environments. This results in narrow applicability and low utility
In this study, we focus on enhancing the policy of a musculoskeletal arm to develop grasping abilities for objects of varying weights. The agent is modeled using MyoSuite, a platform with realistic biomechanics where muscles drive skeletal movement. We observed that optimizing only the control polic
Graph representation learning methods are highly effective in handling complex non-Euclidean data by capturing intricate relationships and features within graph structures. However, traditional methods face challenges when dealing with heterogeneous graphs that contain various types of nodes and edg…
The use of large language models (LLMs) as feature enhancers to optimize node representations, which are then used as inputs for graph neural networks (GNNs), has shown significant potential in graph representation learning. However, the fundamental properties of this approach remain underexplored.…
Large Language Models (LLMs) have achieved remarkable success across various domains. However, they still face significant challenges, including high computational costs for training and limitations in solving complex reasoning problems. Although existing methods have extended the reasoning capabili
A deformable agent can continuously adjust its morphology during training, allowing it to discover more suitable structures and outperform fixed-morphology counterparts in terrain-specific tasks. This adaptability is achieved through a joint optimization process consisting of two stages: the Skeleto
Mixed autonomy traffic systems face significant security challenges when malicious agents disrupt coordination between autonomous and human-driven vehicles. We present Malicious Agent Detection and Isolation (MADI), a framework addressing two critical forms of disruptive behavior: path order violati
Advanced driver assistance systems require a comprehensive understanding of the driver's mental/physical state and traffic context but existing works often neglect the potential benefits of joint learning between these tasks. This paper proposes MMTL-UniAD, a unified multi-modal multi-task learning…
Simplicity is a powerful inductive bias. In reinforcement learning, regularization is used for simpler policies, data augmentation for simpler representations, and sparse reward functions for simpler objectives, all that, with the underlying motivation to increase generalizability and robustness by…
In this paper, we introduce MeshGen, an advanced image-to-3D pipeline that generates high-quality 3D meshes with detailed geometry and physically based rendering (PBR) textures. Addressing the challenges faced by existing 3D native diffusion models, such as suboptimal auto-encoder performance, limit…
In real-world scenarios, many robotic manipulation tasks are hindered by occlusions and limited fields of view, posing significant challenges for passive observation-based models that rely on fixed or wrist-mounted cameras. In this letter, we investigate the problem of robotic manipulation under lim
We introduce ProcWorld, a large-scale benchmark for partially observable embodied spatial reasoning and long-term planning with large language models (LLM) and vision language models (VLM). ProcWorld features a wide range of challenging embodied navigation and object manipulation tasks, covering 16
The growing demands on GPU memory posed by the increasing number of neural network parameters call for training approaches that are more memory-efficient. Previous memory reduction training techniques, such as Low-Rank Adaptation (LoRA) and ReLoRA, face challenges, with LoRA being constrained by its…
Using time-difference-of-arrival (TDOA) measurements from several sensors to locate a target is one of the most widely used wireless localization methods. A challenge for TDOA localization is that the measurement equations are highly nonlinear. The weighted least-squares (WLS) based method tends to…
Multi-task learning (MTL) can advance assistive driving by exploring inter-task correlations through shared representations. However, existing methods face two critical limitations: single-modality constraints limiting comprehensive scene understanding and inefficient architectures impeding real-tim
Tactile sensing plays a critical role in enabling robots to interact safely with target objects in dynamic and unstructured environments. While various tactile sensors based on different sensing principles or different sensitive materials have been proposed, the development of flexible large-area ta…
In this paper, we tackle the task of image captioning with the complementarity of visible light images and infrared images. To address this problem, we propose an RGBIR image fusion captioning model, which can take full advantage of visible light images and infrared images under different conditions…
This paper presents a novel Bionic Soft Finger (BSF) that aims to overcome the limitations of conventional rigid manipulators in terms of adaptability and safety, as well as the challenges faced by soft hands regarding carrying capacity and stability. The BSF design uses a hybrid variable stiffness…
Training an agent to adapt to specific tasks through co-optimization of morphology and control has widely attracted attention. However, whether there exists an optimal configuration and tactics for agents in a multiagent competition scenario is still an issue that is challenging to definitively conc…
Human-robot collaboration (HRC) in a shared workspace has become a common pattern in real-world robot applications and has garnered significant research interest. However, most existing studies for human-in-the-loop (HITL) collaboration with robots in a shared workspace evaluate in either simplified…
Coarse-to-fine schemes are widely used in traditional single-image motion deblur; however in the context of deep learning existing multi-scale algorithms not only require the use of complex modules for feature fusion of low-scale RGB images and deep semantics but also manually generate low-resolutio…
Vision-language models (VLMs) have recently shown promising results in traditional downstream tasks. Evaluation studies have emerged to assess their abilities with the majority focusing on the third-person perspective and only a few addressing specific tasks from the first-person perspective. Howeve…
3D editing plays a crucial role in many areas such as gaming and virtual reality. Traditional 3D editing methods which rely on representations like meshes and point clouds often fall short in realistically depicting complex scenes. On the other hand methods based on implicit 3D representations like…
Multi-agent embodied tasks have been studied in indoor visual environments, but most of the existing research focuses on homogeneous multi-agent tasks. Heterogeneous multi-agent tasks are common in real-world scenarios, and the collaboration strategy among heterogeneous agents with different capabil
In this work, we propose a distributed hierarchical locomotion control strategy for whole-body cooperation and demonstrate the potential for migration into large numbers of agents. Our method utilizes a hierarchical structure to break down complex tasks into smaller, manageable sub-tasks. By incorpo…
Compared with the widely investigated homogeneous multi-robot collaboration, heterogeneous robots with different capabilities can provide a more efficient and flexible collaboration for more complex tasks. In this paper, we consider a more challenging heterogeneous ad hoc teamwork collaboration prob…
Graph Neural Networks (GNNs) demonstrate their significance by effectively modeling complex interrelationships within graph-structured data. To enhance the credibility and robustness of GNNs, it becomes exceptionally crucial to bolster their ability to capture causal relationships. However, despite…
Data-free knowledge distillation is able to utilize the knowledge learned by a large teacher network to augment the training of a smaller student network without accessing the original training data avoiding privacy security and proprietary risks in real applications. In this line of research existi…
Model Predictive Control (MPC) has exhibited remarkable capabilities in optimizing objectives and meeting constraints. However, the substantial computational burden associated with solving the Optimal Control Problem (OCP) at each triggering instant introduces significant delays between state sampli…
It is common for us to feel pressure in a competition environment, which arises from the desire to obtain success comparing with other individuals or opponents. Although we might get anxious under the pressure, it could also be a drive for us to stimulate our potentials to the best in order to keep…
Automatic text-to-3D generation that combines Score Distillation Sampling (SDS) with the optimization of volume rendering has achieved remarkable progress in synthesizing realistic 3D objects. Yet most existing text-to-3D methods by SDS and volume rendering suffer from inaccurate geometry e.g. the J…
Prominent large language models have exhibited human-level performance in many domains, even enabling the derived agents to simulate human and social interactions. While practical works have substantiated the practicability of grounding language agents in sandbox simulation or embodied simulators, c…
Visual Simultaneous Localization and Mapping (SLAM) has been widely adopted in autonomous driving and robotics. While most SLAM systems operate effectively in static or low-dynamic environments, achieving precise pose estimation in diverse unknown dynamic environments continues to pose a significant
Recent progress in vision language foundation models has shown their ability to understand multimodal data and resolve complicated vision language tasks, including robotics manipulation. We seek a straightforward way of making use of existing vision-language models (VLMs) with simple fine-tuning on…
It is critical to perceive physical contact for intelligent robots to safely interact in dynamic, unstructured environments. As physical contacts can occur at any location, a well-performing tactile sensing system should be able to deploy a large area on robotic surface. Some researchers have implem…
Embodied agents are expected to perform more complicated tasks in an interactive environment, with the progress of Embodied AI in recent years. Existing embodied tasks including Embodied Referring Expression (ERE) and other QA-form tasks mainly focuses on interaction in term of linguistic instructio…
Effective access to relevant environmental changes in large human environments is critical for service robots to perform tasks. Since the position of a dynamic goal such as a human is variable, it will be difficult for the robot to locate him accurately. It is worth noting that humans can obtain inf…
Wearable devices have garnered widespread attention as a mobile solution, and various intelligent modules based on wearable devices are increasingly being integrated. Additionally, image captioning is an important task in computer vision that maps images to text. Existing image captioning achievemen…
In this paper, we propose the Masked Space-Time Hash encoding (MSTH), a novel method for efficiently reconstructing dynamic 3D scenes from multi-view or monocular videos. Based on the observation that dynamic scenes often contain substantial static areas that result in redundancy in storage and comp…
Synthesizing high-fidelity videos from real-world multiview input is challenging due to the complexities of real-world environments and high-dynamic movements. Previous works based on neural radiance fields have demonstrated high-quality reconstructions of dynamic scenes. However, training such mode…
It has always been expected that the robot can understand the natural language instruction and thus a more natural human-robot interaction is achieved. Currently, the robot usually interprets the instruction by visually grounding the textual information to its surroundings, while it may be not enoug…
Automatic singing evaluation independent of reference melody is a challenging task due to its subjective and multi-dimensional nature. As an essential attribute of singing voices, vocal timbre has a non-negligible effect and influence on human perception of singing quality. However, no research has…
Optical Music Recognition (OMR) is an important technology in music and has been researched for a long time. Previous approaches for OMR are usually based on CNN for image understanding and RNN for music symbol classification. In this paper, we propose a transformer-based approach with excellent glo…
Referring expressions are commonly used when referring to a specific target in people's daily dialogue. In this paper, we develop a novel task of audio-visual grounding referring expression for robotic manipulation. The robot leverages both the audio and visual information to understand the referrin…
In this letter, we propose a C-shaped bidirectional stiffness joint for an anthropomorphic hand. The spring steel piece is used to connect the knuckles with inner concave C-shape as a rotational joint. The finger is bent by the tendon driven with low stiffness and reset by its own elasticity. The re
Learning multiple tasks sequentially without forgetting previous knowledge, called Continual Learning(CL), remains a long-standing challenge for neural networks. Most existing methods rely on additional network capacity or data replay. In contrast, we introduce a novel approach which we refer to as…
Vision-and-language navigation (VLN) has been an important task in the field of Robotics and Computer Vision. However, most existing vision-and-language navigation models only use features extracted from RGB observation as input, while robots can utilize depth sensors in the real world. Existing res…
In human-robots collaboration scenarios, a human would give robots an instruction that is intuitive for the human himself to accomplish. However, the instruction given to robots is likely ambiguous for them to understand as some information is implicit in the instruction. Therefore, it is necessary…
Many recent works detect 3D objects by several sensor modalities for autonomous driving, where high-resolution cameras and high-line LiDARs are mostly used but relatively expensive. To achieve a balance between overall cost and detection accuracy, many multi-modal fusion techniques have been suggest…
In visual semantic navigation, the robot navigates to a target object with egocentric visual observations and the class label of the target is given. It is a meaningful task inspiring a surge of relevant research. However, most of the existing models are only effective for single-agent navigation, a
Ithas always been a great challenge for the robot to navigate in the visual world following natural language instructions. Recently, several tasks such as the Vision-and-Language Navigation (VLN) and Remote Embodied Visual Referring Expression in Real Indoor Environments (REVERIE) are proposed tryin
Keypoint detection and description play a central role in computer vision. Most existing methods are in the form of scene-level prediction, without returning the object classes of different keypoints. In this paper, we propose the object-centric formulation, which, beyond the conventional setting, r…
Deep reinforcement learning has made significant progress in robotic manipulation tasks and it works well in the ideal disturbance-free environment. However, in a real-world environment, both internal and external disturbances are inevitable, thus the performance of the trained policy will dramatica…
Semantic scene graph provides an effective way for intelligent agents to better understand the environment and it has been extensively used in many robotic applications. Existing work mainly focuses on generating the scene graph from the sensory information collected from a pre-defined path, while t…
Overcoming catastrophic forgetting is of great importance for deep learning and robotics. Recent lifelong learning research has great advances in supervised learning. However, little work focuses on reinforcement learning(RL). We focus on evaluating the performances of state-of-the-art lifelong lear…
Mapping and localization in non-static environments are fundamental problems in robotics. Most of previous methods mainly focus on static and highly dynamic objects in the environment, which may suffer from localization failure in semi-dynamic scenarios without considering objects with lower dynamic…
Reliable real-time extrinsic parameters of 3D Light Detection and Ranging (LiDAR) and camera are a key component of multi-modal perception systems. However, extrinsic transformation may drift gradually during operation, which can result in decreased accuracy of perception system. To solve this probl…
Uncertainty estimation for point cloud semantic segmentation is to quantify the confidence degree for the predicted label of points, which is essential for decision-making tasks. This paper proposes a neighborhood spatial aggregation based method, NSA-MC dropout, to achieve efficient uncertainty est…
Robots are usually equipped with cameras to explore the indoor scene and it is expected that the robot can well describe the scene with natural language. Although some great success has been achieved in image and video captioning technology, especially on many public datasets, the caption generated…
Unsupervised contrastive learning has achieved outstanding success, while the mechanism of contrastive loss has been less studied. In this paper, we concentrate on the understanding of the behaviours of unsupervised contrastive loss. We will show that the contrastive loss is a hardness-aware loss fu…
This paper develops a simple and efficient method for source localization using signal time-of-arrival (TOA) measurements. There exist many TOA localization algorithms, most of which require matrix inversions. Their complexity often makes them unsuitable for cases when a large number of sources need…
We investigate a new AI task --- Multi-Agent Interactive Question Answering --- where several agents explore the scene jointly in interactive environments to answer a question. To cooperate efficiently and answer accurately, agents must be well-organized to have balanced work division and share know…
The sound source separation problem has many useful applications in the field of robotics, such as human-robot interaction, scene understanding, etc. However, it remains a very challenging problem. In this paper, we utilize both visual and audio information of videos to perform the sound source sepa…
Embodiment is an important characteristic for all intelligent agents (creatures and robots), while existing scene description tasks mainly focus on analyzing images passively and the semantic understanding of the scenario is separated from the interaction between the agent and the environment. In th…
Unsupervised learning methods based on contrastive learning have drawn increasing attention and achieved promising results. Most of them aim to learn representations invariant to instance-level variations, which are provided by different views of the same instance. In this paper, we propose Invarian…
Learning manipulations from videos is an inspiriting way for robots to acquire new skills. In this paper, we propose a framework that can generate robotic manipulation plans by observing human demonstration videos without special marks or unnatural demonstrated behaviors. More specifically, the fram…
In this paper, a novel robotic grasping system is established to automatically pick up objects in cluttered scenes. A composite robotic hand composed of a suction cup and a gripper is designed for grasping the object stably. The suction cup is used for lifting the object from the clutter first and t…
This paper studies Learning from Observations (LfO) for imitation learning with access to state-only demonstrations. In contrast to Learning from Demonstration (LfD) that involves both action and state supervisions, LfO is more practical in leveraging previously inapplicable resources (e.g., videos)…
In this work, we investigate the lifelong learning problem from the viewpoint of heterogeneous multi-modal fusion. The main challenges come from the fact that the common representation between heterogeneous modalities should be persistently learned and the learned classifier for each multi-modal tas…
Robots are usually equipped with microphones and cameras to perceive and understand the physical world. Though visual object detection technology has achieved great success, the detection in other modalities remains unsolved. In this paper, we establish a novel robotic sound-indicated visual object…
This paper investigates time-of-arrival (TOA) source node self-positioning with unknown clock skews in wireless sensor networks. For the source-to-anchor direction, source node clock skew does not affect the localization performance. When synchronized anchor nodes simultaneously transmit signals to…
Humans' fingertips can perceive not only the magnitude and the direction of force but also the texture of object. When we grasp an object, the surface texture sensing of the fingertip helps us recognize the object and the force feeling that is parallel to the skin helps us grasp stably. Focusing on…
State-of-the-art object detectors usually learn multi-scale representations to get better results by employing feature pyramids. However, the current designs for feature pyramids are still inefficient to integrate the semantic information over different scales. In this paper, we begin by investigati…
The most state-of-art time-difference-of-arrival (TDOA) localization algorithms are performed under the assumption that all the nodes are synchronized. However, for a widely distributed wireless sensor networks (WSNs), time synchronization between all the nodes is not a trival problem. In this paper…
The robotic grasp detection is a great challenge in the area of robotics. Previous work mainly employs the visual approaches to solve this problem. In this paper, a hybrid deep architecture combining the visual and tactile sensing for robotic grasp detection is proposed. We have demonstrated that th…
In this paper, we exploit the intrinsic relation between different adjective labels and develop a novel multilabel dictionary learning and sparse coding method which is improved by introducing the structured output association information. Such a method makes use of the label correlation information…
We present RON, an efficient and effective framework for generic object detection. Our motivation is to smartly associate the best of the region-based (e.g., Faster R-CNN) and region-free (e.g., SSD) methodologies. Under fully convolutional architecture, RON mainly focuses on two fundamental problem…
Grasp an object from a stack of objects in real-time is still a challenge in robotics. This requires the robot to have the ability of both fast object discovery and grasp detection: a target object should be picked out from the stack first and then a proper grasp configuration is applied to grasp th
Linear Dynamical Systems (LDSs) are the fundamental tools for encoding spatio-temporal data in various disciplines. To enhance the performance of LDSs, in this paper, we address the challenging issue of performing sparse coding on the space of LDSs, where both data and dictionary atoms are LDSs. Rat…
Automatic discovery of topical objects from a set of image collections provides more strong cognitive capability of robot to understand the unstructured environment. In this paper, we propose a novel framework based on dictionary learning for such a task. Different from existing work which utilizes…