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Xiaogang Jia

18 accepted papers

2026

Fourier Features Let Agents Learn High Precision Policies with Imitation Learning

Balázs Gyenes, Emiliyan Gospodinov, Jan Frieling, Enrico Krohmer, Nicolas Schreiber, Xiaogang Jia +2
ICML 2026poster

Various 3D modalities have been proposed for high-precision imitation learning tasks to compensate for the short-comings of RGB-only policies. Modalities that explicitly represent positions in Cartesian space, such as most point cloud encoder architectures, have an inherent advantage over purely ima…

Cited by 0Source ↗code ↗Scholar ↗
2025

BEAST: Efficient Tokenization of B-Splines Encoded Action Sequences for Imitation Learning

Hongyi Zhou, Weiran Liao, Xi Huang, Yucheng Tang, Fabian Otto, Xiaogang Jia +8
NeurIPS 2025poster

We present the B-spline Encoded Action Sequence Tokenizer (BEAST), a novel action tokenizer that encodes action sequences into compact discrete or continuous tokens using B-splines. In contrast to existing action tokenizers based on vector quantization or byte pair encoding, BEAST requires no separ…

Cited by 0Source ↗Scholar ↗
2025

End-to-end Learning of Gaussian Mixture Priors for Diffusion Sampler

Denis Blessing, Xiaogang Jia, Gerhard Neumann
ICLR 2025poster

Diffusion models optimized via variational inference (VI) have emerged as a promising tool for generating samples from unnormalized target densities. These models create samples by simulating a stochastic differential equation, starting from a simple, tractable prior, typically a Gaussian distributi…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Gated Cross-Attention Network for Depth Completion

Xiaogang Jia, Songlei Jian, Yusong Tan, Yonggang Che, Wei Chen, Zhengfa Liang
ICASSP 2025accepted

Depth completion is a popular research direction in the field of depth estimation. The fusion of color and depth features is the critical challenge in this task, mainly due to the asymmetry between the rich scene details in color images and the sparse pixels in depth maps. To tackle this issue, we d…

Cited by 0Source ↗Scholar ↗
2025

IRIS: An Immersive Robot Interaction System

Xinkai Jiang, Qihao Yuan, Enes Ulas Dincer, Hongyi Zhou, Ge Li, Xueyin Li +8
CoRL 2025poster

This paper introduces IRIS, an Immersive Robot Interaction System leveraging Extended Reality (XR). Existing XR-based systems enable efficient data collection but are often challenging to reproduce and reuse due to their specificity to particular robots, objects, simulators, and environments. IRIS a…

Cited by 0Source ↗Scholar ↗
2025

PointMapPolicy: Structured Point Cloud Processing for Multi-Modal Imitation Learning

Xiaogang Jia, Qian Wang, Anrui Wang, Han A. Wang, Balázs Gyenes, Emiliyan Gospodinov +9
NeurIPS 2025poster

Robotic manipulation systems benefit from complementary sensing modalities, where each provides unique environmental information. Point clouds capture detailed geometric structure, while RGB images provide rich semantic context. Current point cloud methods struggle to capture fine-grained detail, es…

Cited by 0Source ↗code ↗Scholar ↗
2024

Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling

Denis Blessing, Xiaogang Jia, Johannes Esslinger, Francisco Vargas, Gerhard Neumann
ICML 2024poster

Monte Carlo methods, Variational Inference, and their combinations play a pivotal role in sampling from intractable probability distributions. However, current studies lack a unified evaluation framework, relying on disparate performance measures and limited method comparisons across diverse tasks,…

Cited by 20Source ↗PDF ↗code ↗Scholar ↗
2024

MaIL: Improving Imitation Learning with Selective State Space Models

Xiaogang Jia, Qian Wang, Atalay Donat, Bowen Xing, Ge Li, Hongyi Zhou +4
CoRL 2024poster

This work introduces Mamba Imitation Learning (MaIL), a novel imitation learning (IL) architecture that offers a computationally efficient alternative to state-of-the-art (SoTA) Transformer policies. Transformer-based policies have achieved remarkable results due to their ability in handling human-r…

Cited by 7Source ↗Scholar ↗
2024

Towards Diverse Behaviors: A Benchmark for Imitation Learning with Human Demonstrations

Xiaogang Jia, Denis Blessing, Xinkai Jiang, Moritz Reuss, Atalay Donat, Rudolf Lioutikov +1
ICLR 2024poster

Imitation learning with human data has demonstrated remarkable success in teaching robots in a wide range of skills. However, the inherent diversity in human behavior leads to the emergence of multi-modal data distributions, thereby presenting a formidable challenge for existing imitation learning a…

Cited by 24Source ↗PDF ↗Scholar ↗
2024

Variational Distillation of Diffusion Policies into Mixture of Experts

Hongyi Zhou, Denis Blessing, Ge Li, Onur Celik, Xiaogang Jia, Gerhard Neumann +1
NeurIPS 2024poster

This work introduces Variational Diffusion Distillation (VDD), a novel method that distills denoising diffusion policies into Mixtures of Experts (MoE) through variational inference. Diffusion Models are the current state-of-the-art in generative modeling due to their exceptional ability to accurate…

Cited by 2Source ↗PDF ↗code ↗Scholar ↗
2023

Dynamic Scenario Representation Learning for Motion Forecasting With Heterogeneous Graph Convolutional Recurrent Networks

Xing Gao, Xiaogang Jia, Yikang Li, Hongkai Xiong
RA-L 2023

Due to the complex and changing interactions in dynamic scenarios, motion forecasting is a challenging problem in autonomous driving. Most existing works exploit static road graphs to characterize scenarios and are limited in modeling evolving spatio-temporal dependencies in dynamic scenarios. In th

Cited by 56Source ↗Scholar ↗
2023

Goal-Conditioned Imitation Learning using Score-based Diffusion Policies

Moritz Reuss, Maximilian Li, Xiaogang Jia, Rudolf Lioutikov
RSS 2023poster

We propose a new policy representation based on score-based diffusion models (SDMs). We apply our new policy representation in the domain of Goal-Conditioned Imitation Learning (GCIL) to learn general-purpose goal-specified policies from large uncurated datasets without rewards. Our new goal-conditi…

Cited by 175Source ↗PDF ↗code ↗Scholar ↗
2023

Information Maximizing Curriculum: A Curriculum-Based Approach for Learning Versatile Skills

Denis Blessing, Onur Celik, Xiaogang Jia, Moritz Reuss, Maximilian Xiling Li, Rudolf Lioutikov +1
NeurIPS 2023poster

Imitation learning uses data for training policies to solve complex tasks. However, when the training data is collected from human demonstrators, it often leads to multimodal distributions because of the variability in human actions. Most imitation learning methods rely on a maximum likelihood (ML)…

Cited by 15Source ↗PDF ↗Scholar ↗
2022

Causal-based Time Series Domain Generalization for Vehicle Intention Prediction

Yeping Hu, Xiaogang Jia, Masayoshi Tomizuka, Wei Zhan
ICRA 2022poster

Accurately predicting the possible behaviors of traffic participants is an essential capability for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically changing environments, they are expected to make accurate predictions regardless of where they are and what driving circu…

Cited by 41Source ↗Scholar ↗
2021

Multi-Scale Cascade Disparity Refinement Stereo Network

Xiaogang Jia, Wei Chen, Zhengfa Liang, Xin Luo, Mingfei Wu, Yusong Tan +1
ICASSP 2021accepted

Stereo matching has attracted much attention in recent years. Traditional methods can quickly generate a disparity result, but the accuracy is low. On the contrary, methods based on neural networks can achieve a high accuracy level, but they are difficult to reach the real-time level. Therefore, thi…

Cited by 0Source ↗Scholar ↗
2021

Multi-Scale Cost Volumes Cascade Network for Stereo Matching

Xiaogang Jia, Wei Chen, Chen Li, Zhengfa Liang, Mingfei Wu, Yusong Tan +1
ICRA 2021poster

Stereo matching is essential for robot navigation. However, the accuracy of current widely used traditional methods is low, while methods based on CNN need expensive computational cost and running time. This is because different cost volumes play a crucial role in balancing speed and accuracy. Thus…

Cited by 8Source ↗Scholar ↗