Author name cluster

Yichen Zhou

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9 papers

2 author rows

IROS Conference 2025 Conference Paper

DSFormer-RTP: Dynamic-stream Transformers for Real-time Deterministic Trajectory Prediction

Xun Chen
Mingxing Wen
Tianchen Deng
Yichen Zhou
Haoyuan Zhang
Danwei Wang

As delivery robots are increasingly integrated into our daily lives, their ability to navigate through crowded spaces demands swift and accurate prediction of pedestrian trajectories, which is crucial for autonomous functionality. However, existing methods face challenges of unstable accuracy and inefficiency in real-world deployment. Trajectory prediction involves both temporal and social dimensions. Recent methods have achieved better results by modeling temporal and social dimensions simultaneously, preventing information loss compared to modeling them separately, which significantly increases computational costs, posing challenges for practical deployment. In this paper, we conceptualize the trajectory prediction task as a deterministic sequence-to-sequence model that produces one precise forecast, aligning with real-world needs while reducing complexity. To improve efficiency and reduce latency for real-time applications, we propose a novel dynamic-stream transformer architecture that categorizes layers into multi-stream and single-stream based on the number of dimensions involved in computation. The single-stream modules attend to all dimensions simultaneously, providing comprehensive information fusion but with higher computational complexity. The multi-stream modules focus on only one dimension, enabling parallel and batched computation, crucial for improving the model’s real-time performance. By combining them strategically, we achieve a balance between accuracy and speed. Extensive experiments on real datasets show that our dynamic-stream transformer architecture significantly reduces computational complexity, achieving a speed increase of 180% to 3180% compared to similar approaches, while also attaining performance close to the state-of-the-art (SOTA) for deterministic trajectory prediction.

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ICML Conference 2025 Conference Paper

In-Context Fine-Tuning for Time-Series Foundation Models

Matthew Faw
Rajat Sen
Yichen Zhou
Abhimanyu Das

Motivated by the recent success of time-series foundation models for zero-shot forecasting, we present a methodology for in-context fine-tuning of a time-series foundation model. In particular, we design a pretrained foundation model that can be prompted (at inference time) with multiple time-series examples, in order to forecast a target time-series into the future. Our foundation model is specifically trained to utilize examples from multiple related time-series in its context window (in addition to the history of the target time-series) to help it adapt to the specific distribution of the target domain at inference time. We show that such a foundation model that uses in-context examples at inference time can obtain much better performance on popular forecasting benchmarks compared to supervised deep learning methods, statistical models, and other time series foundation models. Interestingly, our in-context fine-tuning approach even matches the performance of a foundation model that is explicitly fine-tuned on the target domain.

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ICRA Conference 2025 Conference Paper

LCSPose: Efficient, Accurate and Scalable Markerless 6-DoF Pose Estimation of a Quay Crane Spreader Based on LiDAR and Camera

Yichen Zhou
Jun Zhang 0042
Guohao Peng
Yanpu Yun
Yiyao Liu
Yuanzhe Wang
Danwei Wang

Accurate Six Degrees of Freedom (6-DoF) pose estimation of Ship-To-Shore (STS) quay crane spreaders is crucial for ensuring safe and efficient container handling in port automation. However, existing pose estimation techniques face significant challenges, as camera-based systems either rely on markers, which are prone to damage, or struggle with depth estimation inaccuracies. Additionally, 3D sensor-based approaches, particularly point cloud registration (PCR), face challenges such as initial pose errors, high-latency inference, and difficulties in object identification based purely on geometric features. To address these limitations, we propose LCSPose, a LiDAR-camera fusion-based 6-DoF pose estimation method that is marker-free, accurate, efficient, and scalable. Our approach integrates three key modules: (1) a semantic-geometric segmentation module for spreader segmentation and outlier removal, (2) a spatial consistency template sampling module based on Spatial Consistency Score (SC-Score) for reliable template selection across varying distances, and (3) a multi-view coarse-to-fine pose refinement module which incorporates multi-view PCA alignment for robust initial posture prior estimation and iterative pose refinement strategy for long-range registration. Our method demonstrates a 60% improvement in registration recall over state-of-the-art (SOTA) PCR methods, achieving up to 6 cm in translation error and 0. 19 degrees in rotation error, while maintaining real-time processing at 20Hz.

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ICRA Conference 2025 Conference Paper

Overlapping Free: Anchorless UWB-Assisted Relative Pose Estimation for Multi-Robot Systems

Yanpu Yun
Guohao Peng
Yichen Zhou
Jun Zhang 0042
Yiyao Liu
Kaimin Mao
Danwei Wang

Accurate Relative Pose Estimation (RPE) is critical for effective collaboration of multi-robot systems. Traditional methods using cameras or LiDARs heavily rely on overlapping Fields of View (FoV) between robots, which is highly demanding in practical applications and may hinder collaboration efficiency. To accommodate this issue, we propose Anchorless UWB-Assisted Relative Pose Estimation (AURPE), a novel approach that leverages ultra-wideband (UWB) technology in an anchorless setup to achieve multi-robot RPE without requiring overlapping FoVs or external infrastructure. AURPE first estimates the initial relative poses between robots using inter-robot UWB ranging combined with a Bayesian framework and constrained optimization. During robot operation, AURPE continuously refines the relative poses by integrating UWB measurements with LiDAR-inertial odometry (LIO) and employs a consensus voting mechanism to identify the most reliable pose estimates. Additionally, a pose graph-based backend optimization is incorporated to enhance the accuracy of both initial and real-time relative pose. Extensive simulations and real-world experiments demonstrate that AURPE achieves accurate RPE even in non-overlapping scenarios where traditional methods fail. Compared to state-of-the-art point cloud registration methods, AURPE shows superior performance in both accuracy and robustness, highlighting its potential to significantly enhance cooperative tasks in multi-robot systems operating in complex environments.

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ICML Conference 2024 Conference Paper

A decoder-only foundation model for time-series forecasting

Abhimanyu Das
Weihao Kong
Rajat Sen
Yichen Zhou

Motivated by recent advances in large language models for Natural Language Processing (NLP), we design a time-series foundation model for forecasting whose out-of-the-box zero-shot performance on a variety of public datasets comes close to the accuracy of state-of-the-art supervised forecasting models for each individual dataset. Our model is based on pretraining a decoder style attention model with input patching, using a large time-series corpus comprising both real-world and synthetic datasets. Experiments on a diverse set of previously unseen forecasting datasets suggests that the model can yield accurate zero-shot forecasts across different domains, forecasting horizons and temporal granularities.

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UAI Conference 2023 Conference Paper

Robust distillation for worst-class performance: on the interplay between teacher and student objectives

Serena Wang 0001
Harikrishna Narasimhan
Yichen Zhou
Sara Hooker
Michal Lukasik
Aditya Krishna Menon

Knowledge distillation is a popular technique that has been shown to produce remarkable gains in average accuracy. However, recent work has shown that these gains are not uniform across subgroups in the data, and can often come at the cost of accuracy on rare subgroups and classes. Robust optimization is a common remedy to improve worst-class accuracy in standard learning settings, but in distillation it is unknown whether it is best to apply robust objectives when training the teacher, the student, or both. This work studies the interplay between robust objectives for the teacher and student. Empirically, we show that that jointly modifying the teacher and student objectives can lead to better worst-class student performance and even Pareto improvement in the trade-off between worst-class and overall performance. Theoretically, we show that the per-class calibration of teacher scores is key when training a robust student. Both the theory and experiments support the surprising finding that applying a robust teacher training objective does not always yield a more robust student.

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JMLR Journal 2022 Journal Article

Boulevard: Regularized Stochastic Gradient Boosted Trees and Their Limiting Distribution

Yichen Zhou
Giles Hooker

This paper examines a novel gradient boosting framework for regression. We regularize gradient boosted trees by introducing subsampling and employ a modified shrinkage algorithm so that at every boosting stage the estimate is given by an average of trees. The resulting algorithm, titled "Boulevard'", is shown to converge as the number of trees grows. This construction allows us to demonstrate a central limit theorem for this limit, providing a characterization of uncertainty for predictions. A simulation study and real world examples provide support for both the predictive accuracy of the model and its limiting behavior. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2022. ( edit, beta )

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NeurIPS Conference 2022 Conference Paper

Inception Transformer

Chenyang Si
Weihao Yu
Pan Zhou
Yichen Zhou
Xinchao Wang
Shuicheng Yan

Recent studies show that transformer has strong capability of building long-range dependencies, yet is incompetent in capturing high frequencies that predominantly convey local information. To tackle this issue, we present a novel and general-purpose $\textit{Inception Transformer}$, or $\textit{iFormer}$ for short, that effectively learns comprehensive features with both high- and low-frequency information in visual data. Specifically, we design an Inception mixer to explicitly graft the advantages of convolution and max-pooling for capturing the high-frequency information to transformers. Different from recent hybrid frameworks, the Inception mixer brings greater efficiency through a channel splitting mechanism to adopt parallel convolution/max-pooling path and self-attention path as high- and low-frequency mixers, while having the flexibility to model discriminative information scattered within a wide frequency range. Considering that bottom layers play more roles in capturing high-frequency details while top layers more in modeling low-frequency global information, we further introduce a frequency ramp structure, i. e. , gradually decreasing the dimensions fed to the high-frequency mixer and increasing those to the low-frequency mixer, which can effectively trade-off high- and low-frequency components across different layers. We benchmark the iFormer on a series of vision tasks, and showcase that it achieves impressive performance on image classification, COCO detection and ADE20K segmentation. For example, our iFormer-S hits the top-1 accuracy of 83. 4% on ImageNet-1K, much higher than DeiT-S by 3. 6%, and even slightly better than much bigger model Swin-B (83. 3%) with only 1/4 parameters and 1/3 FLOPs. Code and models are released at https: //github. com/sail-sg/iFormer.

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NeurIPS Conference 2020 Conference Paper

Approximate Heavily-Constrained Learning with Lagrange Multiplier Models

Harikrishna Narasimhan
Andrew Cotter
Yichen Zhou
Serena Wang
Wenshuo Guo

In machine learning applications such as ranking fairness or fairness over intersectional groups, one often encounters optimization problems with an extremely large number of constraints. In particular, with ranking fairness tasks, there may even be a variable number of constraints, e. g. one for each query in the training set. In these cases, the standard approach of optimizing a Lagrangian while maintaining one Lagrange multiplier per constraint may no longer be practical. Our proposal is to associate a feature vector with each constraint, and to learn a ``multiplier model’’ that maps each such vector to the corresponding Lagrange multiplier. We prove optimality, approximate feasibility and generalization guarantees under assumptions on the flexibility of the multiplier model, and empirically demonstrate that our method is effective on real-world case studies.

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