Author name cluster

Bin Xie

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7 papers

2 author rows

AAAI Conference 2026 Conference Paper

SpatialActor: Exploring Disentangled Spatial Representations for Robust Robotic Manipulation

Hao Shi
Bin Xie
Yingfei Liu
Yang Yue
Tiancai Wang
Haoqiang Fan
Xiangyu Zhang
Gao Huang

Robotic manipulation requires precise spatial understanding to interact with objects in the real world. Point-based methods suffer from sparse sampling, leading to the loss of fine-grained semantics. Image-based methods typically feed RGB and depth into 2D backbones pre-trained on 3D auxiliary tasks, but their entangled semantics and geometry are sensitive to inherent depth noise in real-world that disrupts semantic understanding. Moreover, these methods focus on high-level geometry while overlooking low-level spatial cues essential for precise interaction. We propose SpatialActor, a disentangled framework for robust robotic manipulation that explicitly decouples semantics and geometry. The Semantic-guided Geometric Module adaptively fuses two complementary geometry from noisy depth and semantic-guided expert priors. Also, a Spatial Transformer leverages low-level spatial cues for accurate 2D-3D mapping and enables interaction among spatial features. We evaluate SpatialActor on multiple simulation and real-world scenarios across 50+ tasks. It achieves state-of-the-art performance with 87.4% on RLBench and improves by 13.9% to 19.4% under varying noisy conditions, showing strong robustness. Moreover, it significantly enhances few-shot generalization to new tasks and maintains robustness under various spatial perturbations.

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ICLR Conference 2025 Conference Paper

Glad: A Streaming Scene Generator for Autonomous Driving

Bin Xie
Yingfei Liu
Tiancai Wang
Jiale Cao
Xiangyu Zhang 0005

The generation and simulation of diverse real-world scenes have significant application value in the field of autonomous driving, especially for the corner cases. Recently, researchers have explored employing neural radiance fields or diffusion models to generate novel views or synthetic data under driving scenes. However, these approaches suffer from unseen scenes or restricted video length, thus lacking sufficient adaptability for data generation and simulation. To address these issues, we propose a simple yet effective framework, named Glad, to generate video data in a frame-by-frame style. To ensure the temporal consistency of synthetic video, we introduce a latent variable propagation module, which views the latent features of previous frame as noise prior and injects it into the latent features of current frame. In addition, we design a streaming data sampler to orderly sample the original image in a video clip at continuous iterations. Given the reference frame, our Glad can be viewed as a streaming simulator by generating the videos for specific scenes. Extensive experiments are performed on the widely-used nuScenes dataset. Experimental results demonstrate that our proposed Glad achieves promising performance, serving as a strong baseline for online video generation. We will release the source code and models publicly.

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IROS Conference 2025 Conference Paper

Many-Objective Motion Generation Method for Redundant Manipulators by Solving Pathwise Inverse Kinematics

Bin Xie
Jiaming Zhao
Qingfeng Wang
Di Wu 0069

Modern robots are required to operate in complex environments and perform diverse tasks, resulting in redundant degrees of freedom (DoF) for flexibility. However, managing redundancy is challenging due to the high-dimensional and non-convex nature of robotic kinematics. When executing complex tracking tasks, redundant robots must handle non-convex constraints while maintaining many objectives, such as balancing and obstacle avoidance. This paper models the pathwise inverse kinematics of redundant mechanisms as a multi-objective nonlinear optimization problem. We propose an efficient gradient-free optimization method named MoeIK, which demonstrates strong multi-objective balance, rapid global convergence, and adaptability. Our approach enhances the method by integrating relaxation dominance, adaptive interval search strategies, and a restart strategy, significantly improving performance in overcoming many-objective optimization challenges. We compared MoeIK with RelaxedIK, Trac-IK, and BioIK across multiple trajectories on various redundant robots, and the experimental results demonstrate that our algorithm exhibits better multi-objective balance capabilities and supports real-time computation.

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ICLR Conference 2025 Conference Paper

Spa-Bench: a comprehensive Benchmark for Smartphone Agent Evaluation

Jingxuan Chen
Derek Yuen
Bin Xie
Yuhao Yang 0008
Gongwei Chen
Zhihao Wu
Li Yixing
Xurui Zhou

Smartphone agents are increasingly important for helping users control devices efficiently, with (Multimodal) Large Language Model (MLLM)-based approaches emerging as key contenders. Fairly comparing these agents is essential but challenging, requiring a varied task scope, the integration of agents with different implementations, and a generalisable evaluation pipeline to assess their strengths and weaknesses. In this paper, we present SPA-Bench, a comprehensive SmartPhone Agent Benchmark designed to evaluate (M)LLM-based agents in an interactive environment that simulates real-world conditions. SPA-Bench offers three key contributions: (1) A diverse set of tasks covering system and third-party apps in both English and Chinese, focusing on features commonly used in daily routines; (2) A plug-and-play framework enabling real-time agent interaction with Android devices, integrating over ten agents with the flexibility to add more; (3) A novel evaluation pipeline that automatically assesses agent performance across multiple dimensions, encompassing seven metrics related to task completion and resource consumption. Our extensive experiments across tasks and agents reveal challenges like interpreting mobile user interfaces, action grounding, memory retention, and execution costs. We propose future research directions to ease these difficulties, moving closer to real-world smartphone agent applications.

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EAAI Journal 2023 Journal Article

A partial domain adaptation scheme based on weighted adversarial nets with improved CBAM for fault diagnosis of wind turbine gearbox

Yunyi Zhu
Yan Pei
Anqi Wang
Bin Xie
Zheng Qian

Most domain adaptation methods for fault diagnosis depend heavily on the precondition that the source and target domain have an identical label space, which is hard to be satisfied in industrial sites. Recently, many approaches have been developed to implement partial domain adaptation. However, most existing methods adopt classic convolutional neural network as the feature extractor, which limits the ability to learn discriminative representations from non-stationary vibration signals of wind turbine (WT) gearboxes. Moreover, the design of multiple subdomain adaptation will cause complex network structure with many source classes. To address these problems, this paper proposes a partial domain adaptation scheme based on weighted adversarial nets with improved convolutional block attention module (CBAM) for WT gearbox unsupervised fault diagnosis. In detail, a residual convolutional network combining the improved CBAM is designed to extract finer domain discriminative features for knowledge transfer. Meanwhile, a weighting mechanism based on the two-stage domain discriminator is designed to evaluate the contribution of each source sample, through which a simplified transfer network structure is constructed and the source samples unrelated to the target domain can be filtered. Furthermore, an adversarial transfer strategy is introduced to decrease the distribution discrepancy between domains, then the helpful diagnosis knowledge can be transferred. Experiments on two cases demonstrate the superiority and effectiveness of the proposed method compared with existing domain adaptation methods.

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ICRA Conference 2023 Conference Paper

Optimal Parameterized Joints Selection to Improve Motion Planning Performance of Redundant Manipulators

Bin Xie
Qingfeng Wang
Di Wu 0069

The redundant manipulators' analytical solutions can be obtained by the parameterization method. Multiple parameterized joints and their corresponding parametric representations exist for a redundant manipulator. However, how to select the optimal parameterized joints has yet to be well-addressed. This paper delves into the mechanism of the parameterization method and proposes a method to select the optimal parametric representations to improve the motion planning performance of manipulators. We tested the proposed method on an 8-degree-of-freedom (DOF) manipulator. First, all feasible parametric representations are derived, followed by an approach to obtain solution manifolds. We then introduce a metric called the “feasible rate, ” which characterizes the percentage of the solution manifold in the joint space. This metric is used to rapidly assess the influence of different parameterized joints on the manipulator's motion planning performance. To verify the proposed method's correctness, we evaluated the performance of different representations with the MOEA/D algorithm in solving the same path optimization problems based on the algorithm running time and overall motion magnitude of the manipulator. Our simulation results demonstrate that different selections of parameterized joints affect the motion planning performance, and the performance planned by the optimal parametric representation is up to four times greater than that of the worst one.

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ICRA Conference 2020 Conference Paper

Interval Search Genetic Algorithm Based on Trajectory to Solve Inverse Kinematics of Redundant Manipulators and Its Application

Di Wu 0069
Wenting Zhang
Mi Qin
Bin Xie

In this paper, a new method is proposed to solve the inverse kinematics problem of redundant manipulators. This method demonstrates superior performance on continuous motion by combining interval search genetic algorithm based on trajectory which we propose with parametric joint angle method. In this method, population continuity strategy is utilized to improve search speed and reduce evolutionary generation, interval search strategy is introduced to enhance the search ability and overcome the influence of singularity, and reference point strategy is used to avoid sudden changes of joint variables. By introducing those three strategies, this method is especially suitable for redundant manipulators that perform continuous motion. It can not only obtain solutions of inverse kinematics quickly, but also ensure the motion continuity of manipulator and accuracy of the end effector. Moreover, this algorithm can also perform multi-objective tasks by adjusting the fitness function. Finally, this algorithm is applied to an 8 degree of freedom tunnel shotcrete robot. Field experiments and data analysis show that the algorithm can solve the problem quickly in industrial field, and ensure the motion continuity and accuracy.

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