arXiv subjects

Yanyong Zhang

Publications and source records attributed to Yanyong Zhang.

At least 91 records · Page 5Linked to original sources

arXiv2025

STDArm: Transferring Visuomotor Policies From Static Data Training to Dynamic Robot Manipulation

Recent advances in mobile robotic platforms like quadruped robots and drones have spurred a demand for deploying visuomotor policies in increasingly dynamic environments. However, the collection of high-quality training data, the impact of platform motion and processing delays, and limited onboard computing resources pose significant barriers to existing solutions. In this work, we present STDArm, a system that directly transfers policies trained under static conditions to dynamic platforms without extensive modifications. The core of STDArm is a real-time action correction framework consisting of: (1) an action manager to boost control frequency and maintain temporal consistency, (2) a stabilizer with a lightweight prediction network to compensate for motion disturbances, and (3) an online latency estimation module for calibrating system parameters. In this way, STDArm achieves centimeter-level precision in mobile manipulation tasks. We conduct comprehensive evaluations of the proposed STDArm on two types of robotic arms, four types of mobile platforms, and three tasks. Experimental results indicate that the STDArm enables real-time compensation for platform motion disturbances while preserving the original policy's manipulation capabilities, achieving centimeter-level operational precision during robot motion.

cs.RO↗

arXiv2025

A Framework for Benchmarking and Aligning Task-Planning Safety in LLM-Based Embodied Agents

Large Language Models (LLMs) exhibit substantial promise in enhancing task-planning capabilities within embodied agents due to their advanced reasoning and comprehension. However, the systemic safety of these agents remains an underexplored frontier. In this study, we present Safe-BeAl, an integrated framework for the measurement (SafePlan-Bench) and alignment (Safe-Align) of LLM-based embodied agents' behaviors. SafePlan-Bench establishes a comprehensive benchmark for evaluating task-planning safety, encompassing 2,027 daily tasks and corresponding environments distributed across 8 distinct hazard categories (e.g., Fire Hazard). Our empirical analysis reveals that even in the absence of adversarial inputs or malicious intent, LLM-based agents can exhibit unsafe behaviors. To mitigate these hazards, we propose Safe-Align, a method designed to integrate physical-world safety knowledge into LLM-based embodied agents while maintaining task-specific performance. Experiments across a variety of settings demonstrate that Safe-BeAl provides comprehensive safety validation, improving safety by 8.55 - 15.22%, compared to embodied agents based on GPT-4, while ensuring successful task completion.

cs.AI↗

arXiv2025

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving

The significant achievements of pre-trained models leveraging large volumes of data in the field of NLP and 2D vision inspire us to explore the potential of extensive data pre-training for 3D perception in autonomous driving. Toward this goal, this paper proposes to utilize massive unlabeled data from heterogeneous datasets to pre-train 3D perception models. We introduce a self-supervised pre-training framework that learns effective 3D representations from scratch on unlabeled data, combined with a prompt adapter based domain adaptation strategy to reduce dataset bias. The approach significantly improves model performance on downstream tasks such as 3D object detection, BEV segmentation, 3D object tracking, and occupancy prediction, and shows steady performance increase as the training data volume scales up, demonstrating the potential of continually benefit 3D perception models for autonomous driving. We will release the source code to inspire further investigations in the community.

cs.CV↗

arXiv2025

CAFE-AD: Cross-Scenario Adaptive Feature Enhancement for Trajectory Planning in Autonomous Driving

Imitation learning based planning tasks on the nuPlan dataset have gained great interest due to their potential to generate human-like driving behaviors. However, open-loop training on the nuPlan dataset tends to cause causal confusion during closed-loop testing, and the dataset also presents a long-tail distribution of scenarios. These issues introduce challenges for imitation learning. To tackle these problems, we introduce CAFE-AD, a Cross-Scenario Adaptive Feature Enhancement for Trajectory Planning in Autonomous Driving method, designed to enhance feature representation across various scenario types. We develop an adaptive feature pruning module that ranks feature importance to capture the most relevant information while reducing the interference of noisy information during training. Moreover, we propose a cross-scenario feature interpolation module that enhances scenario information to introduce diversity, enabling the network to alleviate over-fitting in dominant scenarios. We evaluate our method CAFE-AD on the challenging public nuPlan Test14-Hard closed-loop simulation benchmark. The results demonstrate that CAFE-AD outperforms state-of-the-art methods including rule-based and hybrid planners, and exhibits the potential in mitigating the impact of long-tail distribution within the dataset. Additionally, we further validate its effectiveness in real-world environments. The code and models will be made available at https://github.com/AlniyatRui/CAFE-AD.

cs.RO↗

arXiv2025

T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge

The deployment of Large Language Models (LLMs) on edge devices is increasingly important to enhance on-device intelligence. Weight quantization is crucial for reducing the memory footprint of LLMs on devices. However, low-bit LLMs necessitate mixed precision matrix multiplication (mpGEMM) of low precision weights and high precision activations during inference. Existing systems, lacking native support for mpGEMM, resort to dequantize weights for high precision computation. Such an indirect way can lead to a significant inference overhead. In this paper, we introduce T-MAC, an innovative lookup table(LUT)-based method designed for efficient low-bit LLM (i.e., weight-quantized LLM) inference on CPUs. T-MAC directly supports mpGEMM without dequantization, while simultaneously eliminating multiplications and reducing additions required. Specifically, T-MAC transforms the traditional data-type-centric multiplication to bit-wise table lookup, and enables a unified and scalable mpGEMM solution. Our LUT-based kernels scale linearly to the weight bit-width. Evaluated on low-bit Llama and BitNet models, T-MAC demonstrates up to 4x increase in throughput and 70% reduction in energy consumption compared to llama.cpp. For BitNet-b1.58-3B, T-MAC delivers a token generation throughput of 30 tokens/s with a single core and 71 tokens/s with eight cores on M2-Ultra, and 11 tokens/s on lower-end devices like Raspberry Pi 5, which significantly exceeds the adult average reading speed. T-MAC with LUT-based computing paradigm, paves the way for the practical deployment of low-bit LLMs on resource-constrained edge devices without compromising computational efficiency. The system is open-sourced at https://github.com/microsoft/T-MAC .

cs.DC↗

arXiv2025

RaCFormer: Towards High-Quality 3D Object Detection via Query-based Radar-Camera Fusion

We propose Radar-Camera fusion transformer (RaCFormer) to boost the accuracy of 3D object detection by the following insight. The Radar-Camera fusion in outdoor 3D scene perception is capped by the image-to-BEV transformation--if the depth of pixels is not accurately estimated, the naive combination of BEV features actually integrates unaligned visual content. To avoid this problem, we propose a query-based framework that enables adaptive sampling of instance-relevant features from both the bird's-eye view (BEV) and the original image view. Furthermore, we enhance system performance by two key designs: optimizing query initialization and strengthening the representational capacity of BEV. For the former, we introduce an adaptive circular distribution in polar coordinates to refine the initialization of object queries, allowing for a distance-based adjustment of query density. For the latter, we initially incorporate a radar-guided depth head to refine the transformation from image view to BEV. Subsequently, we focus on leveraging the Doppler effect of radar and introduce an implicit dynamic catcher to capture the temporal elements within the BEV. Extensive experiments on nuScenes and View-of-Delft (VoD) datasets validate the merits of our design. Remarkably, our method achieves superior results of 64.9% mAP and 70.2% NDS on nuScenes. RaCFormer also secures the state-of-the-art performance on the VoD dataset. Code is available at https://github.com/cxmomo/RaCFormer.

cs.CV↗

arXiv2025

MT-PCR: Leveraging Modality Transformation for Large-Scale Point Cloud Registration with Limited Overlap

Large-scale scene point cloud registration with limited overlap is a challenging task due to computational load and constrained data acquisition. To tackle these issues, we propose a point cloud registration method, MT-PCR, based on Modality Transformation. MT-PCR leverages a BEV capturing the maximal overlap information to improve the accuracy and utilizes images to provide complementary spatial features. Specifically, MT-PCR converts 3D point clouds to BEV images and eastimates correspondence by 2D image keypoints extraction and matching. Subsequently, the 2D correspondence estimates are then transformed back to 3D point clouds using inverse mapping. We have applied MT-PCR to Terrestrial Laser Scanning and Aerial Laser Scanning point cloud registration on the GrAco dataset, involving 8 low-overlap, square-kilometer scale registration scenarios. Experiments and comparisons with commonly used methods demonstrate that MT-PCR can achieve superior accuracy and robustness in large-scale scenes with limited overlap.

cs.RO↗

arXiv2025

OccMamba: Semantic Occupancy Prediction with State Space Models

Training deep learning models for semantic occupancy prediction is challenging due to factors such as a large number of occupancy cells, severe occlusion, limited visual cues, complicated driving scenarios, etc. Recent methods often adopt transformer-based architectures given their strong capability in learning input-conditioned weights and long-range relationships. However, transformer-based networks are notorious for their quadratic computation complexity, seriously undermining their efficacy and deployment in semantic occupancy prediction. Inspired by the global modeling and linear computation complexity of the Mamba architecture, we present the first Mamba-based network for semantic occupancy prediction, termed OccMamba. Specifically, we first design the hierarchical Mamba module and local context processor to better aggregate global and local contextual information, respectively. Besides, to relieve the inherent domain gap between the linguistic and 3D domains, we present a simple yet effective 3D-to-1D reordering scheme, i.e., height-prioritized 2D Hilbert expansion. It can maximally retain the spatial structure of 3D voxels as well as facilitate the processing of Mamba blocks. Endowed with the aforementioned designs, our OccMamba is capable of directly and efficiently processing large volumes of dense scene grids, achieving state-of-the-art performance across three prevalent occupancy prediction benchmarks, including OpenOccupancy, SemanticKITTI, and SemanticPOSS. Notably, on OpenOccupancy, our OccMamba outperforms the previous state-of-the-art Co-Occ by 5.1% IoU and 4.3% mIoU, respectively. Our implementation is open-sourced and available at: https://github.com/USTCLH/OccMamba.

cs.CV↗

arXiv2025

S3R-GS: Streamlining the Pipeline for Large-Scale Street Scene Reconstruction

Recently, 3D Gaussian Splatting (3DGS) has reshaped the field of photorealistic 3D reconstruction, achieving impressive rendering quality and speed. However, when applied to large-scale street scenes, existing methods suffer from rapidly escalating per-viewpoint reconstruction costs as scene size increases, leading to significant computational overhead. After revisiting the conventional pipeline, we identify three key factors accounting for this issue: unnecessary local-to-global transformations, excessive 3D-to-2D projections, and inefficient rendering of distant content. To address these challenges, we propose S3R-GS, a 3DGS framework that Streamlines the pipeline for large-scale Street Scene Reconstruction, effectively mitigating these limitations. Moreover, most existing street 3DGS methods rely on ground-truth 3D bounding boxes to separate dynamic and static components, but 3D bounding boxes are difficult to obtain, limiting real-world applicability. To address this, we propose an alternative solution with 2D boxes, which are easier to annotate or can be predicted by off-the-shelf vision foundation models. Such designs together make S3R-GS readily adapt to large, in-the-wild scenarios. Extensive experiments demonstrate that S3R-GS enhances rendering quality and significantly accelerates reconstruction. Remarkably, when applied to videos from the challenging Argoverse2 dataset, it achieves state-of-the-art PSNR and SSIM, reducing reconstruction time to below 50%--and even 20%--of competing methods.

cs.CV↗

arXiv2025

OG-Gaussian: Occupancy Based Street Gaussians for Autonomous Driving

Accurate and realistic 3D scene reconstruction enables the lifelike creation of autonomous driving simulation environments. With advancements in 3D Gaussian Splatting (3DGS), previous studies have applied it to reconstruct complex dynamic driving scenes. These methods typically require expensive LiDAR sensors and pre-annotated datasets of dynamic objects. To address these challenges, we propose OG-Gaussian, a novel approach that replaces LiDAR point clouds with Occupancy Grids (OGs) generated from surround-view camera images using Occupancy Prediction Network (ONet). Our method leverages the semantic information in OGs to separate dynamic vehicles from static street background, converting these grids into two distinct sets of initial point clouds for reconstructing both static and dynamic objects. Additionally, we estimate the trajectories and poses of dynamic objects through a learning-based approach, eliminating the need for complex manual annotations. Experiments on Waymo Open dataset demonstrate that OG-Gaussian is on par with the current state-of-the-art in terms of reconstruction quality and rendering speed, achieving an average PSNR of 35.13 and a rendering speed of 143 FPS, while significantly reducing computational costs and economic overhead.

cs.CV↗

arXiv2024

Perception Helps Planning: Facilitating Multi-Stage Lane-Level Integration via Double-Edge Structures

When planning for autonomous driving, it is crucial to consider essential traffic elements such as lanes, intersections, traffic regulations, and dynamic agents. However, they are often overlooked by the traditional end-to-end planning methods, likely leading to inefficiencies and non-compliance with traffic regulations. In this work, we endeavor to integrate the perception of these elements into the planning task. To this end, we propose Perception Helps Planning (PHP), a novel framework that reconciles lane-level planning with perception. This integration ensures that planning is inherently aligned with traffic constraints, thus facilitating safe and efficient driving. Specifically, PHP focuses on both edges of a lane for planning and perception purposes, taking into consideration the 3D positions of both lane edges and attributes for lane intersections, lane directions, lane occupancy, and planning. In the algorithmic design, the process begins with the transformer encoding multi-camera images to extract the above features and predicting lane-level perception results. Next, the hierarchical feature early fusion module refines the features for predicting planning attributes. Finally, the double-edge interpreter utilizes a late-fusion process specifically designed to integrate lane-level perception and planning information, culminating in the generation of vehicle control signals. Experiments on three Carla benchmarks show significant improvements in driving score of 27.20%, 33.47%, and 15.54% over existing algorithms, respectively, achieving the state-of-the-art performance, with the system operating up to 22.57 FPS.

cs.CV↗

arXiv2024

RayFormer: Improving Query-Based Multi-Camera 3D Object Detection via Ray-Centric Strategies

The recent advances in query-based multi-camera 3D object detection are featured by initializing object queries in the 3D space, and then sampling features from perspective-view images to perform multi-round query refinement. In such a framework, query points near the same camera ray are likely to sample similar features from very close pixels, resulting in ambiguous query features and degraded detection accuracy. To this end, we introduce RayFormer, a camera-ray-inspired query-based 3D object detector that aligns the initialization and feature extraction of object queries with the optical characteristics of cameras. Specifically, RayFormer transforms perspective-view image features into bird's eye view (BEV) via the lift-splat-shoot method and segments the BEV map to sectors based on the camera rays. Object queries are uniformly and sparsely initialized along each camera ray, facilitating the projection of different queries onto different areas in the image to extract distinct features. Besides, we leverage the instance information of images to supplement the uniformly initialized object queries by further involving additional queries along the ray from 2D object detection boxes. To extract unique object-level features that cater to distinct queries, we design a ray sampling method that suitably organizes the distribution of feature sampling points on both images and bird's eye view. Extensive experiments are conducted on the nuScenes dataset to validate our proposed ray-inspired model design. The proposed RayFormer achieves superior performance of 55.5% mAP and 63.3% NDS, respectively.

cs.CV↗

arXiv2024

Map++: Towards User-Participatory Visual SLAM Systems with Efficient Map Expansion and Sharing

Constructing precise 3D maps is crucial for the development of future map-based systems such as self-driving and navigation. However, generating these maps in complex environments, such as multi-level parking garages or shopping malls, remains a formidable challenge. In this paper, we introduce a participatory sensing approach that delegates map-building tasks to map users, thereby enabling cost-effective and continuous data collection. The proposed method harnesses the collective efforts of users, facilitating the expansion and ongoing update of the maps as the environment evolves. We realized this approach by developing Map++, an efficient system that functions as a plug-and-play extension, supporting participatory map-building based on existing SLAM algorithms. Map++ addresses a plethora of scalability issues in this participatory map-building system by proposing a set of lightweight, application-layer protocols. We evaluated Map++ in four representative settings: an indoor garage, an outdoor plaza, a public SLAM benchmark, and a simulated environment. The results demonstrate that Map++ can reduce traffic volume by approximately 46% with negligible degradation in mapping accuracy, i.e., less than 0.03m compared to the baseline system. It can support approximately $2 \times$ as many concurrent users as the baseline under the same network bandwidth. Additionally, for users who travel on already-mapped trajectories, they can directly utilize the existing maps for localization and save 47% of the CPU usage.

cs.CV↗

arXiv2024

Harnessing Your DRAM and SSD for Sustainable and Accessible LLM Inference with Mixed-Precision and Multi-level Caching

Although Large Language Models (LLMs) have demonstrated remarkable capabilities, their massive parameter counts and associated extensive computing make LLMs' deployment the main part of carbon emission from nowadays AI applications. Compared to modern GPUs like H$100$, it would be significantly carbon-sustainable if we could leverage old-fashioned GPUs such as M$40$ (as shown in Figure 1, M$40$ only has one third carbon emission of H$100$'s) for LLM servings. However, the limited High Bandwidth Memory (HBM) available on such GPU often cannot support the loading of LLMs due to the gigantic model size and intermediate activation data, making their serving challenging. For instance, a LLaMA2 model with $70$B parameters typically requires $128$GB for inference, which substantially surpasses $24$GB HBM in a $3090$ GPU and remains infeasible even considering the additional $64$GB DRAM. To address this challenge, this paper proposes a mixed-precision with a model modularization algorithm to enable LLM inference on outdated hardware with resource constraints. (The precision denotes the numerical precision like FP16, INT8, INT4) and multi-level caching (M2Cache).) Specifically, our M2Cache first modulizes neurons in LLM and creates their importance ranking. Then, it adopts a dynamic sparse mixed-precision quantization mechanism in weight space to reduce computational demands and communication overhead at each decoding step. It collectively lowers the operational carbon emissions associated with LLM inference. Moreover, M2Cache introduces a three-level cache management system with HBM, DRAM, and SSDs that complements the dynamic sparse mixed-precision inference. To enhance communication efficiency, M2Cache maintains a neuron-level mixed-precision LRU cache in HBM, a larger layer-aware cache in DRAM, and a full model in SSD.

cs.LG↗

arXiv2024

CELLmap: Enhancing LiDAR SLAM through Elastic and Lightweight Spherical Map Representation

SLAM is a fundamental capability of unmanned systems, with LiDAR-based SLAM gaining widespread adoption due to its high precision. Current SLAM systems can achieve centimeter-level accuracy within a short period. However, there are still several challenges when dealing with largescale mapping tasks including significant storage requirements and difficulty of reusing the constructed maps. To address this, we first design an elastic and lightweight map representation called CELLmap, composed of several CELLs, each representing the local map at the corresponding location. Then, we design a general backend including CELL-based bidirectional registration module and loop closure detection module to improve global map consistency. Our experiments have demonstrated that CELLmap can represent the precise geometric structure of large-scale maps of KITTI dataset using only about 60 MB. Additionally, our general backend achieves up to a 26.88% improvement over various LiDAR odometry methods.

cs.RO↗

arXiv2024

MHRC: Closed-loop Decentralized Multi-Heterogeneous Robot Collaboration with Large Language Models

The integration of large language models (LLMs) with robotics has significantly advanced robots' abilities in perception, cognition, and task planning. The use of natural language interfaces offers a unified approach for expressing the capability differences of heterogeneous robots, facilitating communication between them, and enabling seamless task allocation and collaboration. Currently, the utilization of LLMs to achieve decentralized multi-heterogeneous robot collaborative tasks remains an under-explored area of research. In this paper, we introduce a novel framework that utilizes LLMs to achieve decentralized collaboration among multiple heterogeneous robots. Our framework supports three robot categories, mobile robots, manipulation robots, and mobile manipulation robots, working together to complete tasks such as exploration, transportation, and organization. We developed a rich set of textual feedback mechanisms and chain-of-thought (CoT) prompts to enhance task planning efficiency and overall system performance. The mobile manipulation robot can adjust its base position flexibly, ensuring optimal conditions for grasping tasks. The manipulation robot can comprehend task requirements, seek assistance when necessary, and handle objects appropriately. Meanwhile, the mobile robot can explore the environment extensively, map object locations, and communicate this information to the mobile manipulation robot, thus improving task execution efficiency. We evaluated the framework using PyBullet, creating scenarios with three different room layouts and three distinct operational tasks. We tested various LLM models and conducted ablation studies to assess the contributions of different modules. The experimental results confirm the effectiveness and necessity of our proposed framework.

cs.RO↗

arXiv2024

PoIFusion: Multi-Modal 3D Object Detection via Fusion at Points of Interest

In this work, we present PoIFusion, a conceptually simple yet effective multi-modal 3D object detection framework to fuse the information of RGB images and LiDAR point clouds at the points of interest (PoIs). Different from the most accurate methods to date that transform multi-sensor data into a unified view or leverage the global attention mechanism to facilitate fusion, our approach maintains the view of each modality and obtains multi-modal features by computation-friendly projection and interpolation. In particular, our PoIFusion follows the paradigm of query-based object detection, formulating object queries as dynamic 3D boxes and generating a set of PoIs based on each query box. The PoIs serve as the keypoints to represent a 3D object and play the role of the basic units in multi-modal fusion. Specifically, we project PoIs into the view of each modality to sample the corresponding feature and integrate the multi-modal features at each PoI through a dynamic fusion block. Furthermore, the features of PoIs derived from the same query box are aggregated together to update the query feature. Our approach prevents information loss caused by view transformation and eliminates the computation-intensive global attention, making the multi-modal 3D object detector more applicable. We conducted extensive experiments on nuScenes and Argoverse2 datasets to evaluate our approach. Remarkably, the proposed approach achieves state-of-the-art results on both datasets without any bells and whistles, \emph{i.e.}, 74.9\% NDS and 73.4\% mAP on nuScenes, and 31.6\% CDS and 40.6\% mAP on Argoverse2. The code will be made available at \url{https://djiajunustc.github.io/projects/poifusion}.

cs.CV↗

arXiv2024

LFP: Efficient and Accurate End-to-End Lane-Level Planning via Camera-LiDAR Fusion

Multi-modal systems enhance performance in autonomous driving but face inefficiencies due to indiscriminate processing within each modality. Additionally, the independent feature learning of each modality lacks interaction, which results in extracted features that do not possess the complementary characteristics. These issue increases the cost of fusing redundant information across modalities. To address these challenges, we propose targeting driving-relevant elements, which reduces the volume of LiDAR features while preserving critical information. This approach enhances lane level interaction between the image and LiDAR branches, allowing for the extraction and fusion of their respective advantageous features. Building upon the camera-only framework PHP, we introduce the Lane-level camera-LiDAR Fusion Planning (LFP) method, which balances efficiency with performance by using lanes as the unit for sensor fusion. Specifically, we design three modules to enhance efficiency and performance. For efficiency, we propose an image-guided coarse lane prior generation module that forecasts the region of interest (ROI) for lanes and assigns a confidence score, guiding LiDAR processing. The LiDAR feature extraction modules leverages lane-aware priors from the image branch to guide sampling for pillar, retaining essential pillars. For performance, the lane-level cross-modal query integration and feature enhancement module uses confidence score from ROI to combine low-confidence image queries with LiDAR queries, extracting complementary depth features. These features enhance the low-confidence image features, compensating for the lack of depth. Experiments on the Carla benchmarks show that our method achieves state-of-the-art performance in both driving score and infraction score, with maximum improvement of 15% and 14% over existing algorithms, respectively, maintaining high frame rate of 19.27 FPS.

cs.CV↗