21 accepted papers
This paper presents a non-cooperative source localization approach based on received signal strength (RSS) and 2D environment map, considering both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. Conventional localization methods, e.g., weighted centroid localization (WCL), may perform…
Non-prehensile (NP) manipulation, in which robots alter object states without forming stable grasps (for example, pushing, poking, or sliding), significantly broadens robotic manipulation capabilities when grasping is infeasible or insufficient. However, enabling a unified framework that generalizes…
Towards human-robot coexistence, socially aware navigation is significant for mobile robots. Yet existing studies on this area focus mainly on path efficiency and pedestrian collision avoidance, which are essential but represent only a fraction of social navigation. Beyond these basics, robots must …
Assembly hinges on reliably forming connections between parts; yet most robotic approaches plan assembly sequences and part poses while treating connectors as an afterthought. Connections represent the foundational physical constraints of assembly execution; while task planning sequences operations,…
Bimanual manipulation is a fundamental robotic skill that requires continuous and precise coordination between two arms. While imitation learning (IL) is the dominant paradigm for acquiring this capability, existing approaches, whether robot-centric or object-centric, often overlook the dynamic geom…
While significant research has focused on developing embodied reasoning capabilities using Vision-Language Models (VLMs) or integrating advanced VLMs into Vision-Language-Action (VLA) models for end-to-end robot control, few studies directly address the critical gap between upstream VLM-based reason…
Articulated object manipulation remains a critical challenge in robotics due to the complex kinematic constraints and the limited physical reasoning of existing methods. In this work, we introduce ArtGS, a novel framework that extends 3D Gaussian Splatting (3DGS) by integrating visual-physical model
Heterogeneous multi-robot systems (HMRS) have emerged as a powerful ap- proach for tackling complex tasks that single robots cannot manage alone. Current large-language-model-based multi-agent systems (LLM-based MAS) have shown success in areas like software development and operating systems, but ap…
Humans possess an extraordinary ability to understand and execute complex manipulation tasks by interpreting abstract instruction manuals. For robots, however, this capability remains a substantial challenge, as they lack the ability to interpret abstract instructions and translate them into executa…
The rapid progress of navigation, manipulation, and vision models has made mobile manipulators capable in many specialized tasks. However, the open-world mobile manipulation (OWMM) task remains a challenge due to the need for generalization to open-ended instructions and environments, as well as th…
Radio map estimation (RME) aims to construct a map of radio strength across multiple domains (e.g., space and frequency) from limited measurements. Data-driven deep neural model-based RME showed promising performance, yet requiring excessive training resources. This work puts forth an RME approach t…
Constructing channel state information (CSI) maps may help wireless communications and localization. However, CSI map construction requires up-to-date CSI measurement data with location labels, which induces a huge challenge in practice. Conventional CSI embedding methods project the CSI to a low di…
Robotic behavior synthesis, the problem of understanding multimodal inputs and generating precise physical control for robots, is an important part of Embodied AI. Despite successes in applying multimodal large language models for high-level understanding, it remains challenging to translate these c…
This paper addresses the off-grid tensor-guided interpolation problem, aiming to reconstruct a 3D power spectrum map from sparse observations. A segmented polynomial model is employed to handle off-grid measurements, while a nuclear norm regularization is incorporated to account for the inherent low…
Object goal navigation is an important problem in Embodied AI that involves guiding the agent to navigate to an instance of the object category in an unknown environment---typically an indoor scene. Unfortunately, current state-of-the-art methods for this problem rely heavily on data-driven approach…
This paper develops a regression assisted matrix completion method to reconstruct the propagation field for received signal strength (RSS) based source localization without prior knowledge of the propagation model. Existing matrix completion methods did not exploit the fact that the uncertainty of e…
Herein, the problem of non-parametric source localization based on signal strength measured at different sensor locations is examined. A recently developed matrix-based method is considered. This method first arranges the measurements into an observation matrix based on a uniform grid defined in the…
Knowing the location of a transceiver may enable advanced radio resource management strategies in sensing and communication networks. However, there are many scenarios where users operate in a non-cooperative mode with no localization-dedicated signaling with the network. As a result, conventional t…
The ability for a wireless network to precisely localize the radio nodes composing it is a great tool towards system optimization and is increasingly seen as a basic service requirement. In the past, model-free algorithms such as weighted centroid localization (WCL) have proved popular, especially i…
Unimodality-constrained matrix or tensor factorization has applications in various domains, such as non-parametric source localization and data clustering, where the signals of interest are unimodal. Such factorizations are challenged by the non-convex nature of unimodality constraints. This paper d…
This paper studies the problem of localizing a source based on different types of signals measured at different sensing locations, where propagation models of the signals are not known. A tensor observation model is proposed to arrange such multimodal data into different layers to form a 3D data arr…