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Haoang Li

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AAAI Conference 2026 Conference Paper

ReconVLA: Reconstructive Vision-Language-Action Model as Effective Robot Perceiver

  • Wenxuan Song
  • Ziyang Zhou
  • Han Zhao
  • Jiayi Chen
  • Pengxiang Ding
  • Haodong Yan
  • Yuxin Huang
  • Feilong Tang

Recent advances in Vision-Language-Action (VLA) models have enabled robotic agents to integrate multimodal understanding with action execution. However, our empirical analysis reveals that current VLAs struggle to allocate visual attention to target regions. Instead, visual attention is always dispersed. To guide the visual attention grounding on the correct target, we propose ReconVLA, a reconstructive VLA model with an implicit grounding paradigm. Conditioned on the model's visual outputs, a diffusion transformer aims to reconstruct the gaze region of the image, which corresponds to the target manipulated objects. This process prompts the VLA model to learn fine-grained representations and accurately allocate visual attention, thus effectively leveraging task-specific visual information and conducting precise manipulation. Moreover, we curate a large-scale pretraining dataset comprising over 100k trajectories and 2 million data samples from open-source robotic datasets, further boosting the model’s generalization in visual reconstruction. Extensive experiments in simulation and the real world demonstrate the superiority of our implicit grounding method, showcasing its capabilities of precise manipulation and generalization.

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IROS Conference 2025 Conference Paper

Interactive Navigation for Legged Manipulators with Learned Arm-Pushing Controller

  • Zhihai Bi
  • Kai Chen 0006
  • Chunxin Zheng
  • Yulin Li 0001
  • Haoang Li
  • Jun Ma 0008

Interactive navigation is crucial in scenarios where proactively interacting with objects can yield shorter paths, thus significantly improving traversal efficiency. Existing methods primarily focus on using the robot body to relocate obstacles during navigation. However, they prove ineffective in narrow or constrained spaces where the robot’s dimensions restrict its manipulation capabilities. This paper introduces a novel interactive navigation framework for legged manipulators, featuring an active arm-pushing mechanism that enables the robot to reposition movable obstacles in space-constrained environments. To this end, we develop a reinforcement learning-based arm-pushing controller with a two-stage reward strategy for object manipulation. Specifically, this strategy first directs the manipulator to a designated pushing zone to achieve a kinematically feasible contact configuration. Then, the end effector is guided to maintain its position at appropriate contact points for stable object displacement while preventing toppling. The simulations validate the robustness of the arm-pushing controller, showing that the two-stage reward strategy improves policy convergence and long-term performance. Real-World experiments further demonstrate the effectiveness of the proposed navigation framework, which achieves shorter paths and reduced traversal time. The open-source project can be found at https://zhihaibi.github.io/interactive-push.github.io/.

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IROS Conference 2025 Conference Paper

L2COcc: Lightweight Camera-Centric Semantic Scene Completion via Distillation of LiDAR Model

  • Ruoyu Wang
  • Yukai Ma
  • Yi Yao
  • Sheng Tao
  • Haoang Li
  • Zongzhi Zhu
  • Yong Liu 0007
  • Xingxing Zuo 0001

Semantic Scene Completion (SSC) constitutes a pivotal element in autonomous driving perception systems, tasked with inferring the 3D semantic occupancy of a scene from sensory data. To improve accuracy, prior research has implemented various computationally demanding and memory-intensive 3D operations, imposing significant computational requirements on the platform during training and testing. This paper proposes L2COcc, a lightweight camera-centric SSC framework that also accommodates LiDAR inputs. With our proposed efficient voxel transformer (EVT) and cross-modal knowledge modules, including feature similarity distillation (FSD), TPV distillation (TPVD) and prediction alignment distillation (PAD), our method substantially reduce computational burden while maintaining high accuracy. The experimental evaluations demonstrate that our proposed method surpasses the current state-of-the-art vision-based SSC methods regarding accuracy on both the SemanticKITTI and SSCBench-KITTI-360 benchmarks, respectively. Additionally, our method is more lightweight, exhibiting a reduction in both memory consumption and inference time by over 23% compared to the current state-of-the-arts method. Code is available at our project page: https://studyingfufu.github.io/L2COcc/.

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IROS Conference 2025 Conference Paper

PD-VLA: Accelerating Vision-Language-Action Model Integrated with Action Chunking via Parallel Decoding

  • Wenxuan Song
  • Jiayi Chen
  • Pengxiang Ding
  • Han Zhao 0008
  • Wei Zhao
  • Zhide Zhong
  • Zongyuan Ge
  • Zhijun Li

Vision-Language-Action (VLA) models demonstrate remarkable potential for generalizable robotic manipulation. The performance of VLA models can be improved by integrating with action chunking, a critical technique for effective control. However, action chunking linearly scales up action dimensions in VLA models with increased chunking sizes. This reduces the inference efficiency. Therefore, accelerating VLA integrated with action chunking is an urgent need. To tackle this problem, we propose PD-VLA, the first parallel decoding framework for VLA models integrated with action chunking. Our framework reformulates autoregressive decoding as a nonlinear system solved by parallel fixed-point iterations. This approach preserves model performance with mathematical guarantees while significantly improving decoding speed. In addition, it enables training-free acceleration without architectural changes, as well as seamless synergy with existing acceleration techniques. Extensive simulations validate that our PD-VLA maintains competitive success rates while achieving 2. 52× execution frequency on manipulators (with 7 degrees of freedom) compared with the fundamental VLA model. Furthermore, we experimentally identify the most effective settings for acceleration. Finally, real-world experiments validate its high applicability across different tasks.

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IROS Conference 2025 Conference Paper

RMG: Real-Time Expressive Motion Generation with Self-collision Avoidance for 6-DOF Companion Robotic Arms

  • Jiansheng Li
  • Haotian Song
  • Haoang Li
  • Jinni Zhou
  • Qiang Nie
  • Yi Cai

The six-degree-of-freedom (6-DOF) robotic arm has gained widespread application in human-coexisting environments. While previous research has predominantly focused on functional motion generation, the critical aspect of expressive motion in human-robot interaction remains largely unexplored. This paper presents a novel real-time motion generation planner that enhances interactivity by creating expressive robotic motions between arbitrary start and end states within predefined time constraints. Our approach involves three key contributions: first, we develop a mapping algorithm to construct an expressive motion dataset derived from human dance movements; second, we train motion generation models in both Cartesian and joint spaces using this dataset; third, we introduce an optimization algorithm that guarantees smooth, collision-free motion while maintaining the intended expressive style. Experimental results demonstrate the effectiveness of our method, which can generate expressive and generalized motions in under 0. 5 seconds while satisfying all specified constraints.

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IROS Conference 2025 Conference Paper

RoboDexVLM: Visual Language Model-Enabled Task Planning and Motion Control for Dexterous Robot Manipulation

  • Haichao Liu 0003
  • Sikai Guo
  • Pengfei Mai
  • Jiahang Cao
  • Haoang Li
  • Jun Ma 0008

This paper introduces RoboDexVLM, an innovative framework for robot task planning and grasp detection tailored for a collaborative manipulator equipped with a dexterous hand. Previous methods focus on simplified and limited manipulation tasks, which often neglect the complexities associated with grasping a diverse array of objects in a long-horizon manner. In contrast, our proposed framework utilizes a dexterous hand capable of grasping objects of varying shapes and sizes while executing tasks based on natural language commands. The proposed approach has the following core components: First, a robust task planner with a task-level recovery mechanism that leverages vision-language models (VLMs) is designed, which enables the system to interpret and execute open-vocabulary commands for long sequence tasks. Second, a language-guided dexterous grasp perception algorithm is presented based on robot kinematics and formal methods, tailored for zero-shot dexterous manipulation with diverse objects and commands. Comprehensive experimental results validate the effectiveness, adaptability, and robustness of RoboDexVLM in handling long-horizon scenarios and performing dexterous grasping. These results highlight the framework’s ability to operate in complex environments, showcasing its potential for open-vocabulary dexterous manipulation. Our open-source project page can be found at https://henryhcliu.github.io/robodexvlm.

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IROS Conference 2025 Conference Paper

SkyVLN: Vision-and-Language Navigation and NMPC Control for UAVs in Urban Environments

  • Tianshun Li
  • Tianyi Huai
  • Zhen Li
  • Yichun Gao
  • Haoang Li
  • Xinhu Zheng

Unmanned Aerial Vehicles (UAVs) have emerged as versatile tools across various sectors, driven by their mobility and adaptability. This paper introduces SkyVLN, a novel framework integrating vision-and-language navigation (VLN) with Nonlinear Model Predictive Control (NMPC) to enhance UAV autonomy in complex urban environments. Unlike traditional navigation methods, SkyVLN leverages Large Language Models (LLMs) to interpret natural language instructions and visual observations, enabling UAVs to navigate through dynamic 3D spaces with improved accuracy and robustness. We present a multimodal navigation agent equipped with a fine-grained spatial verbalizer and a history path memory mechanism. These components allow the UAV to disambiguate spatial contexts, handle ambiguous instructions, and backtrack when necessary. The framework also incorporates an NMPC module for dynamic obstacle avoidance, ensuring precise trajectory tracking and collision prevention. To validate our approach, we developed a high-fidelity 3D urban simulation environment using AirSim, featuring realistic imagery and dynamic urban elements. Extensive experiments demonstrate that SkyVLN significantly improves navigation success rates and efficiency, particularly in new and unseen environments.

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IROS Conference 2025 Conference Paper

STG-Avatar: Animatable Human Avatars via Spacetime Gaussian

  • Guangan Jiang
  • Tianzi Zhang
  • Dong Li
  • Zhenjun Zhao
  • Haoang Li
  • Mingrui Li
  • Hongyu Wang 0001

Realistic animatable human avatars from monocular videos are crucial for advancing human-robot interaction and enhancing immersive virtual experiences. While recent research on 3DGS-based human avatars has made progress, it still struggles with accurately representing detailed features of non-rigid objects (e. g. , clothing deformations) and dynamic regions (e. g. , rapidly moving limbs). To address these challenges, we present STG-Avatar, a 3DGS-based framework for high-fidelity animatable human avatar reconstruction. Specifically, our framework introduces a rigid-nonrigid coupled deformation framework that synergistically integrates Spacetime Gaussians (STG) with linear blend skinning (LBS). In this hybrid design, LBS enables real-time skeletal control by driving global pose transformations, while STG complements it through spacetime-adaptive optimization of 3D Gaussians. Furthermore, we employ optical flow to identify high-dynamic regions and guide the adaptive densification of 3D Gaussians in these regions. Experimental results demonstrate that our method consistently outperforms state-of-the-art baselines in both reconstruction quality and operational efficiency, achieving superior quantitative metrics while retaining real-time rendering capabilities. Our code is available at https://github.com/jiangguangan/STG-Avatar

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IROS Conference 2024 Conference Paper

Physically-Based Photometric Bundle Adjustment in Non-Lambertian Environments

  • Lei Cheng
  • Junpeng Hu
  • Haodong Yan
  • Mariia Gladkova
  • Tianyu Huang
  • Yun-Hui Liu 0001
  • Daniel Cremers
  • Haoang Li

Photometric bundle adjustment (PBA) is widely used in estimating the camera pose and 3D geometry by assuming a Lambertian world. However, the assumption of photometric consistency is often violated since the non-diffuse reflection is common in real-world environments. The photometric inconsistency significantly affects the reliability of existing PBA methods. To solve this problem, we propose a novel physically-based PBA method. Specifically, we introduce the physically-based weights regarding material, illumination, and light path. These weights distinguish the pixel pairs with different levels of photometric inconsistency. We also design corresponding models for material estimation based on sequential images and illumination estimation based on point clouds. In addition, we establish the first SLAM-related dataset of non-Lambertian scenes with complete ground truth of illumination and material. Extensive experiments demonstrated that our PBA method outperforms existing approaches in accuracy.

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ICRA Conference 2020 Conference Paper

A Synchronization Approach for Achieving Cooperative Adaptive Cruise Control Based Non-Stop Intersection Passing

  • Zhe Liu 0022
  • Huanshu Wei
  • Hanjiang Hu
  • Chuanzhe Suo
  • Hesheng Wang 0001
  • Haoang Li
  • Yun-Hui Liu 0001

Cooperative adaptive cruise control (CACC) of intelligent vehicles contributes to improving cruise control performance, reducing traffic congestion, saving energy and increasing traffic flow capacity. In this paper, we resolve the CACC problem from the viewpoint of synchronization control, our main idea is to introduce the spatial-temporal synchronization mechanism into vehicle platoon control to achieve the robust CACC and to further realize the non-stop intersection control. Firstly, by introducing the cross-coupling based space synchronization mechanism, a distributed control algorithm is presented to achieve the single-lane CACC in the presence of vehicle-to-vehicle (V2V) communications, which enables autonomous vehicles to track the desired platoon trajectory while synchronizing their longitudinal velocities to keeping the expected inter-vehicle distance. Secondly, by designing the enter-time scheduling mechanism (temporal synchronization), a high-level intersection control strategy is proposed to command vehicles to form a virtual platoon to pass through the intersection without stopping. Thirdly, a Lyapunov-based time-domain stability analysis approach is presented. Compared with the traditional string stability based approach, the proposed approach guarantees the global asymptotical convergence of the proposed CACC system. Experiments in the small-scale simulated system demonstrate the effectiveness of the proposed approach.

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IROS Conference 2020 Conference Paper

CUHK-AHU Dataset: Promoting Practical Self-Driving Applications in the Complex Airport Logistics, Hill and Urban Environments

  • Wen Chen 0021
  • Zhe Liu 0022
  • Hongchao Zhao
  • Shunbo Zhou
  • Haoang Li
  • Yun-Hui Liu 0001

This paper presents a novel dataset targeting three types of challenging environments for autonomous driving, i. e. , the industrial logistics environment, the undulating hill environment and the mixed complex urban environment. To the best of the author’s knowledge, similar dataset has not been published in the existing public datasets, especially for the logistics environment collected in the functioning Hong Kong Air Cargo Terminal (HACT). Structural changes always suddenly appeared in the airport logistics environment due to the frequent movement of goods in and out. In the structureless and noisy hill environment, the non-flat plane movement is usual. In the mixed complex urban environment, the highly dynamic residence blocks, sloped roads and highways are included in a single collection. The presented dataset includes LiDAR, image, IMU and GPS data by repeatedly driving along several paths to capture the structural changes, the illumination changes and the different degrees of undulation of the roads. The baseline trajectories are provided which are estimated by Simultaneous Localization and Mapping (SLAM).

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IROS Conference 2020 Conference Paper

End-to-End 3D Point Cloud Learning for Registration Task Using Virtual Correspondences

  • Huanshu Wei
  • Zhijian Qiao
  • Zhe Liu 0022
  • Chuanzhe Suo
  • Peng Yin 0001
  • Yueling Shen
  • Haoang Li
  • Hesheng Wang 0001

3D Point cloud registration is still a very challenging topic due to the difficulty in finding the rigid transformation between two point clouds with partial correspondences, and it's even harder in the absence of any initial estimation information. In this paper, we present an end-to-end deep-learning based approach to resolve the point cloud registration problem. Firstly, the revised LPD-Net is introduced to extract features and aggregate them with the graph network. Secondly, the self-attention mechanism is utilized to enhance the structure information in the point cloud and the cross-attention mechanism is designed to enhance the corresponding information between the two input point clouds. Based on which, the virtual corresponding points can be generated by a soft pointer based method, and finally, the point cloud registration problem can be solved by implementing the SVD method. Comparison results in ModelNet40 dataset validate that the proposed approach reaches the state-of-the-art in point cloud registration tasks and experiment resutls in KITTI dataset validate the effectiveness of the proposed approach in real applications.

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ICRA Conference 2020 Conference Paper

Robust and Efficient Estimation of Absolute Camera Pose for Monocular Visual Odometry

  • Haoang Li
  • Wen Chen 0021
  • Ji Zhao 0001
  • Jean-Charles Bazin
  • Lei Luo
  • Zhe Liu 0022
  • Yun-Hui Liu 0001

Given a set of 3D-to-2D point correspondences corrupted by outliers, we aim to robustly estimate the absolute camera pose. Existing methods robust to outliers either fail to guarantee high robustness and efficiency simultaneously, or require an appropriate initial pose and thus lack generality. In contrast, we propose a novel approach based on the robust "L 2 -minimizing estimate" (L 2 E) loss. We first define a novel cost function by integrating the projection constraint into the L 2 E loss. Then to efficiently obtain the global minimum of this function, we propose a hybrid strategy of a local optimizer and branch-and-bound. For branch-and-bound, we derive effective function bounds. Our approach can handle high outlier ratios, leading to high robustness. It can run reliably regardless of whether the initial pose is appropriate, providing high generality. Moreover, given a decent initial pose, it is suitable for real-time applications. Experiments on synthetic and real-world datasets showed that our approach outperforms state-of-the-art methods in terms of robustness and/or efficiency.

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ICRA Conference 2019 Conference Paper

A Hierarchical Framework for Coordinating Large-Scale Robot Networks

  • Zhe Liu 0022
  • Shunbo Zhou
  • Hesheng Wang 0001
  • Yi Shen
  • Haoang Li
  • Yun-Hui Liu 0001

In this paper, we study the cooperative path planning and motion coordination problems of the multi-robot system with large number of robots, aiming for practical applications in robotic warehouses and automated transportation systems. Particularly, we solve the life-long planning problem and guarantee the coordination performance in the presence of robot motion uncertainties. A hierarchical path planning and motion coordination structure is presented. The environment is divided into several sectors and a traffic heat-map is presented to describe the current sector-level traffic condition. In path planning level, the sector-level path is calculated by considering the path distance, the current traffic condition and the current robot uncertainty. In motion coordination level, local cooperative A* algorithm and conflict-based searching strategy are utilized within each sector to generate the collision-free local path of each robot in a rolling planning manner. The effectiveness and practical applicability of the proposed approach are validated by simulations with more than one thousand robots and real experiments.

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ICRA Conference 2019 Conference Paper

Leveraging Structural Regularity of Atlanta World for Monocular SLAM

  • Haoang Li
  • Yazhou Xing
  • Ji Zhao 0001
  • Jean-Charles Bazin
  • Zhe Liu 0022
  • Yun-Hui Liu 0001

A wide range of man-made environments can be abstracted as the Atlanta world. It consists of a set of Atlanta frames with a common vertical (gravitational) axis and multiple horizontal axes orthogonal to this vertical axis. This paper focuses on leveraging the regularity of Atlanta world for monocular SLAM. First, we robustly cluster image lines. Based on these clusters, we compute the local Atlanta frames in the camera frame by solving polynomial equations. Our method provides the global optimum and satisfies inherent geometric constraints. Second, we define the posterior probabilities to refine the initial clusters and Atlanta frames alternately by the maximum a posteriori estimation. Third, based on multiple local Atlanta frames, we compute the global Atlanta frames in the world frame using Kalman filtering. We optimize rotations by the global alignment and then refine translations and 3D line-based map under the directional constraints. Experiments on both synthesized and real data have demonstrated that our approach outperforms state-of-the-art methods.

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IROS Conference 2019 Conference Paper

Line-based Absolute and Relative Camera Pose Estimation in Structured Environments

  • Haoang Li
  • Ji Zhao 0001
  • Jean-Charles Bazin
  • Wen Chen 0021
  • Kai Chen 0028
  • Yun-Hui Liu 0001

3D lines in structured environments encode particular regularity like parallelism and orthogonality. We leverage this structural regularity to estimate the absolute and relative camera poses. We decouple the rotation and translation, and propose a novel rotation estimation method. We decompose the absolute and relative rotations and reformulate the problem as computing the rotation from the Manhattan frame to the camera frame. To compute this rotation, we propose an accurate and efficient two-step method. We first estimate its two degrees of freedom (DOF) by two image lines, and then estimate its third DOF by another image line. For these lines, we assume their associated 3D lines are mutually orthogonal, or two 3D lines are parallel to each other and orthogonal to the third. Thanks to our two-step DOF estimation, our absolute and relative pose estimation methods are accurate and efficient. Moreover, our relative pose estimation method relies on weaker assumptions or less correspondences than existing approaches. We also propose a novel strategy to reject outliers and identify dominant directions of the scene. We integrate it into our pose estimation methods, and show that it is more robust than RANSAC. Experiments on synthetic and real-world datasets demonstrated that our methods outperform state-of-the-art approaches.

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ICRA Conference 2018 Conference Paper

A Monocular SLAM System Leveraging Structural Regularity in Manhattan World

  • Haoang Li
  • Jian Yao 0002
  • Jean-Charles Bazin
  • Xiaohu Lu
  • Yazhou Xing
  • Kang Liu 0003

The structural features in Manhattan world encode useful geometric information of parallelism, orthogonality and/or coplanarity in the scene. By fully exploiting these structural features, we propose a novel monocular SLAM system which provides accurate estimation of camera poses and 3D map. The foremost contribution of the proposed system is a structural feature-based optimization module which contains three novel optimization strategies. First, a rotation optimization strategy using the parallelism and orthogonality of 3D lines is presented. We propose a global binding method to compute an accurate estimation of the absolute rotation of the camera. Then we propose an approach for calculating the relative rotation to further refine the absolute rotation. Second, a translation optimization strategy leveraging coplanarity is proposed. Coplanar features are effectively identified, and we leverage them by a unified model handling both points and lines to calculate the relative translation, and then the optimal absolute translation. Third, a 3D line optimization strategy utilizing parallelism, orthogonality and coplanarity simultaneously is proposed to obtain an accurate 3D map consisting of structural line segments with low computational complexity. Experiments in man-made environments have demonstrated that the proposed system outperforms existing state-of-the-art monocular SLAM systems in terms of accuracy and robustness.

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IROS Conference 2018 Conference Paper

Robust Camera Pose Estimation via Consensus on Ray Bundle and Vector Field

  • Haoang Li
  • Ji Zhao 0001
  • Jean-Charles Bazin
  • Lei Luo
  • Junlin Wu
  • Jian Yao 0002

Estimating the camera pose requires point correspondences. However, in practice, correspondences are inevitably corrupted by outliers, which affects the pose estimation. We propose a general and accurate outlier removal strategy for robust camera pose estimation. The proposed strategy can detect outliers by leveraging the fact that only inliers comply with two effective consensuses, i. e. , 3D ray bundle consensus and 2D vector field consensus. Our strategy has a nested structure. First, the outer module utilizes the 3D ray bundle consensus. We define the likelihood based on the probabilistic mixture model and maximize it by the expectation-maximization (EM) algorithm. The inlier probability of each correspondence and the camera pose are determined alternately. Second, the inner module exploits the 2D vector field consensus to refine the probabilities obtained by the outer module. The refinement based on the Bayesian rule facilitates the convergence of the outer module and improves the accuracy of the entire framework. Our strategy can be integrated into various existing camera pose estimation methods which are originally vulnerable to outliers. Experiments on both synthesized data and real images have shown that our approach outperforms state-of-the-art outlier rejection methods in terms of accuracy and robustness.

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IROS Conference 2017 Conference Paper

Combining points and lines for camera pose estimation and optimization in monocular visual odometry

  • Haoang Li
  • Jian Yao 0002
  • Xiaohu Lu
  • Junlin Wu

In this paper, we propose a unified model for camera pose estimation and a novel strategy for pose optimization by combining points and lines in monocular visual odometry. Our proposed unified model treats point and line features equivalently, which is applicable for all the minimal cases requiring the minimum number 3 of point or/and line features and can be easily extended for various circumstances with more additional observations. The core idea is to directly retrieve all stationary points of a cost function which is minimized by the first-order optimality condition without initialization or iteration. The estimated pose is reliable due to robust geometric constraints and the reliable algebraic solver. To refine the camera pose, we propose a novel optimization strategy to minimize the unconstrained Sampson error by taking specific uncertainty for each feature into account to penalize noise more reasonably. Moreover, it is simpler than the conventional bundle adjustment by avoiding the high-dimensional parameter searching. Experimental results on simulated data and real images have sufficiently demonstrated the superiority of our proposed camera pose estimation and optimization method by comparing with state-of-the-art monocular algorithms.

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v2026.09.13