26 accepted papers
Manipulation in confined and cluttered environments remains a significant challenge due to partial observability and complex configuration spaces. Effective manipulation in such environments requires an intelligent exploration strategy to safely understand the scene and search the target. In this pa…
While the Transformer architecture dominates many fields, its quadratic self-attention complexity hinders its use in large-scale applications. **Linear attention** offers an efficient alternative, but its direct application often degrades performance, with existing fixes typically re-introducing com…
In environments where robots operate with limited global navigation satellite system accessibility, ultra-wideband (UWB) localization technology is a popular auxiliary solution to assist visual–inertial odometry systems. However, current UWB approaches lack 3-D pairwise localization capability and s…
We introduce SANA-Video, a small diffusion model that can efficiently generate videos up to 720×1280 resolution and minute-length duration. SANA-Video synthesizes high-resolution, high-quality and long videos with strong text-video alignment at a remarkably fast speed, deployable on RTX 5090 GPU. Tw…
While scaling laws for imitation learning have primarily focused on generalization in open-world settings, the relationship between data and precision in closed-world tasks like robotic assembly remains largely unexplored. This paper systematically investigates this relationship and introduces a nov…
We introduce DC-AR, a novel masked autoregressive (AR) text-to-image generation framework that delivers superior image generation quality with exceptional computational efficiency. Due to the tokenizers' limitations, prior masked AR models have lagged behind diffusion models in terms of quality or e…
Accurate and real-time stereo depth estimation is important for autonomous robots, such as autonomous aerial vehicles (AAVs). Due to the computation constraints of these miniaturized robots, current state-of-the-art algorithms deploy light-weight neural networks while using self-supervised online ad
Collaborative perception in unknown environments is crucial for multi-robot systems. With the emergence of foundation models, robots can now not only perceive geometric information but also achieve open-vocabulary scene understanding. However, existing map representations that support open-vocabular
This paper presents SANA-1.5, a linear Diffusion Transformer for efficient scaling in text-to-image generation. Building upon SANA-1.0, we introduce three key innovations: (1) Efficient Training Scaling: A depth-growth paradigm that enables scaling from 1.6B to 4.8B parameters with significantly red…
This paper presents SANA-Sprint, an efficient diffusion model for ultra-fast text-to-image (T2I) generation. SANA-Sprint is built on a pre-trained foundation model and augmented with hybrid distillation, dramatically reducing inference steps from 20 to 1-4.We introduce three key innovations: (1) We…
Ultra-wideband (UWB) is a popular technology for robotic localization in global positioning system (GPS)-challenged and vision-obstructed scenarios. In UWB localization systems, distance information is extracted from ToA and ToD timestamp measurements. However, these measurements are easily influenc…
Ultra-wide band (UWB) systems are gaining popularity for multi-robot localization benefiting from their high-accuracy ranging capabilities. However, current UWB systems fall short in determining orientations and realizing pair-wise localization for neglecting bearing information. Given the importanc…
Large language models (LLMs) have pushed the limits of natural language understanding and exhibited excellent problem-solving ability. Despite the great success, most existing open-source LLMs (\eg, LLaMA-2) are still far away from satisfactory for solving mathematical problems due to the complex re…
The most advanced text-to-image (T2I) models require significant training costs (e.g., millions of GPU hours), seriously hindering the fundamental innovation for the AIGC community while increasing CO2 emissions. This paper introduces PixArt-$\alpha$, a Transformer-based T2I diffusion model whose im…
Although the neural radiance field (NeRF) exhibits high-fidelity visualization on the rendering task, it still suffers from rendering defects, especially in complex scenes. In this paper, we delve into the reason for the unsatisfactory performance and conjecture that it comes from interference in th…
Multi-agent reinforcement learning (MARL) performs well for solving complex cooperative tasks when the scenarios have well-defined dense rewards. However, there are usually sparse reward settings in many real-world multi-agent systems, which makes it difficult for MARL algorithms to successfully lea…
Planes, lines, and cylinders widely exist in man-made environments. This letter introduces a LiDAR simultaneous localization and mapping (SLAM) system using those three types of landmarks. Our algorithm has three components including local mapping, global mapping, and localization. The local and glo
Robots often need to accomplish complex tasks in unknown environments, which is a challenging problem, involving autonomous exploration for acquiring necessary scene knowledge and task planning. In traditional approaches, the agent first explores the environment to instantiate a complete planning do
Fig. 5b in [1] shows the geometric center of vertices in robots after map merging, but it shows the situation before map merging. The correct figure of Fig. 5 in [1] is shown in Fig. 1
Autonomous exploration and mapping of unknown terrains employing single or multiple robots is an essential task in mobile robotics and has therefore been widely investigated. Nevertheless, given the lack of unified data sets, metrics, and platforms to evaluate the exploration approaches, we develop…
Collaborative perception in unknown environments is a critical task for multi-robot systems. Without external positioning, multi-robot mapping systems have relied on the transfer of place recognition (PR) descriptors or sensor data for the relative pose estimation (RelPose) and share their local map
Multi-robot exploration in unknown environments is a fundamental task for a multi-robot system, involving inter-robot communication through messages among the robots. However, in a restricted communication environment, the limited communication resources become the system's bottleneck due to a large
Disaster areas involving floods and earthquakes are commonly large, with the rescue time being quite tight, suggesting multi-Unmanned Aerial Vehicles (UAV) exploration rather than employing a single UAV. For such scenarios, current UAV exploration is modeled as a Coverage Path Planning (CPP) problem…
Localization and navigation are basic robotic tasks requiring an accurate and up-to-date map to finish these tasks, with crowdsourced data to detect map changes posing an appealing solution. Collecting and processing crowdsourced data requires low-cost sensors and algorithms, but existing methods re
Collaborative exploration in an unknown environment without external positioning under limited communication is an essential task for multi-robot applications. For inter-robot positioning, various Distributed Simultaneous Localization and Mapping (DSLAM) systems share the Place Recognition (PR) desc…
Learning depth and ego-motion from unlabeled videos via self-supervision from epipolar projection can improve the robustness and accuracy of the 3D perception and localization of vision-based robots. However, the rigid projection computed by ego-motion cannot represent all scene points, such as poin