Zhenyu Lu

Xiyao Liu, Haitao Chang, Fei Hui et al.

Angular velocity estimation is critical for eddy current de-tumbling of noncooperative space targets. However, unknown model of the noncooperative target and few observation data make the model-based estimation methods challenged. In this paper, a Data-driven Moving Horizon Estimation method is proposed to estimate the angular velocity of the noncooperative target with de-tumbling torque. In this method, model-free state estimation of the angular velocity can be achieved using only one historical trajectory data that satisfies the rank condition. With local linear approximation, the Willems fundamental lemma is extended to nonlinear autonomous systems, and the rank condition for the historical trajectory data is deduced. Then, a data-driven moving horizon estimation algorithm based on the M step Lyapunov function is designed, and the time-discount robust stability of the algorithm is given. In order to illustrate the effectiveness of the proposed algorithm, experiments and simulations are performed to estimate the angular velocity in eddy current de-tumbling with only de-tumbling torque measurement.

Xinyao Shu, Shiyang Yan, Xu Yang et al.

Visual question answering (VQA) is a critical multimodal task in which an agent must answer questions according to the visual cue. Unfortunately, language bias is a common problem in VQA, which refers to the model generating answers only by associating with the questions while ignoring the visual content, resulting in biased results. We tackle the language bias problem by proposing a self-supervised counterfactual metric learning (SC-ML) method to focus the image features better. SC-ML can adaptively select the question-relevant visual features to answer the question, reducing the negative influence of question-irrelevant visual features on inferring answers. In addition, question-irrelevant visual features can be seamlessly incorporated into counterfactual training schemes to further boost robustness. Extensive experiments have proved the effectiveness of our method with improved results on the VQA-CP dataset. Our code will be made publicly available.

Yi Wang, Zeyu Xue, Mujie Liu et al.

Teleoperation via natural-language reduces operator workload and enhances safety in high-risk or remote settings. However, in dynamic remote scenes, transmission latency during bidirectional communication creates gaps between remote perceived states and operator intent, leading to command misunderstanding and incorrect execution. To mitigate this, we introduce the Spatio-Temporal Open-Vocabulary Scene Graph (ST-OVSG), a representation that enriches open-vocabulary perception with temporal dynamics and lightweight latency annotations. ST-OVSG leverages LVLMs to construct open-vocabulary 3D object representations, and extends them into the temporal domain via Hungarian assignment with our temporal matching cost, yielding a unified spatio-temporal scene graph. A latency tag is embedded to enable LVLM planners to retrospectively query past scene states, thereby resolving local-remote state mismatches caused by transmission delays. To further reduce redundancy and highlight task-relevant cues, we propose a task-oriented subgraph filtering strategy that produces compact inputs for the planner. ST-OVSG generalizes to novel categories and enhances planning robustness against transmission latency without requiring fine-tuning. Experiments show that our method achieves 74 percent node accuracy on the Replica benchmark, outperforming ConceptGraph. Notably, in the latency-robustness experiment, the LVLM planner assisted by ST-OVSG achieved a planning success rate of 70.5 percent.

Zhenyu Lu, Lakshay Sethi

Previous methods for audio-image matching generally fall into one of two categories: pipeline models or End-to-End models. Pipeline models first transcribe speech and then encode the resulting text; End-to-End models encode speech directly. Generally, pipeline models outperform end-to-end models, but the intermediate transcription necessarily discards some potentially useful non-textual information. In addition to textual information, speech can convey details such as accent, mood, and and emphasis, which should be effectively captured in the encoded representation. In this paper, we investigate whether non-textual information, which is overlooked by pipeline-based models, can be leveraged to improve speech-image matching performance. We thoroughly analyze and compare End-to-End models, pipeline models, and our proposed dual-channel model for robust audio-image retrieval on a variety of datasets. Our approach achieves a substantial performance gain over the previous state-of-the-art by leveraging strong pretrained models, a prompting mechanism and a bifurcated design.