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Xiaoqi Li

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8 papers

1 author row

NeurIPS Conference 2025 Conference Paper

AC-DiT: Adaptive Coordination Diffusion Transformer for Mobile Manipulation

Sixiang Chen
Jiaming Liu
Siyuan Qian
Han Jiang
Zhuoyang Liu
Chenyang Gu
Xiaoqi Li
Chengkai Hou

Recently, mobile manipulation has attracted increasing attention for enabling language-conditioned robotic control in household tasks. However, existing methods still face challenges in coordinating mobile base and manipulator, primarily due to two limitations. On the one hand, they fail to explicitly model the influence of the mobile base on manipulator control, which easily leads to error accumulation under high degrees of freedom. On the other hand, they treat the entire mobile manipulation process with the same visual observation modality (e. g. , either all 2D or all 3D), overlooking the distinct multimodal perception requirements at different stages during mobile manipulation. To address this, we propose the Adaptive Coordination Diffusion Transformer (AC-DiT), which enhances mobile base and manipulator coordination for end-to-end mobile manipulation. First, since the motion of the mobile base directly influences the manipulator's actions, we introduce a mobility-to-body conditioning mechanism that guides the model to first extract base motion representations, which are then used as context prior for predicting whole-body actions. This enables whole-body control that accounts for the potential impact of the mobile base’s motion. Second, to meet the perception requirements at different stages of mobile manipulation, we design a perception-aware multimodal conditioning strategy that dynamically adjusts the fusion weights between various 2D visual images and 3D point clouds, yielding visual features tailored to the current perceptual needs. This allows the model to, for example, adaptively rely more on 2D inputs when semantic information is crucial for action prediction, while placing greater emphasis on 3D geometric information when precise spatial understanding is required. We empirically validate AC-DiT through extensive experiments on both simulated and real-world mobile manipulation tasks, demonstrating superior performance compared to existing methods.

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NeurIPS Conference 2025 Conference Paper

Fast-in-Slow: A Dual-System VLA Model Unifying Fast Manipulation within Slow Reasoning

Hao Chen
Jiaming Liu
Chenyang Gu
Zhuoyang Liu
Renrui Zhang
Xiaoqi Li
Xiao He
Yandong Guo

Generalized policy and execution efficiency constitute the two critical challenges in robotic manipulation. While recent foundation policies benefit from the common-sense reasoning capabilities of internet-scale pretrained vision-language models (VLMs), they often suffer from low execution frequency. To mitigate this dilemma, dual-system approaches have been proposed to leverage a VLM-based System 2 module for handling high-level decision-making, and a separate System 1 action module for ensuring real-time control. However, existing designs maintain both systems as separate models, limiting System 1 from fully leveraging the rich pretrained knowledge from the VLM-based System 2. In this work, we propose Fast-in-Slow (FiS), a unified dual-system vision-language-action (VLA) model that embeds the System 1 execution module within the VLM-based System 2 by partially sharing parameters. This innovative paradigm not only enables high-frequency execution in System 1, but also facilitates coordination between multimodal reasoning and execution components within a single foundation model of System 2. Given their fundamentally distinct roles within FiS-VLA, we design the two systems to incorporate heterogeneous modality inputs alongside asynchronous operating frequencies, enabling both fast and precise manipulation. To enable coordination between the two systems, a dual-aware co-training strategy is proposed that equips System 1 with action generation capabilities while preserving System 2’s contextual understanding to provide stable latent conditions for System 1. For evaluation, FiS-VLA outperforms previous state-of-the-art methods by 8% in simulation and 11% in real-world tasks in terms of average success rate, while achieving a 117. 7 Hz control frequency with action chunk set to eight. Project web page: https: //fast-in-slow. github. io.

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AAAI Conference 2025 Conference Paper

LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding

Senqiao Yang
Jiaming Liu
Renrui Zhang
Mingjie Pan
Ziyu Guo
Xiaoqi Li
Zehui Chen
Peng Gao

Recently, Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have shown promise in instruction following and image understanding. While these models are powerful, they have not yet been developed to comprehend the more challenging 3D geometric and physical scenes, especially when it comes to the sparse outdoor LiDAR data. In this paper, we introduce LiDAR-LLM, which takes raw LiDAR data as input and harnesses the remarkable reasoning capabilities of LLMs to gain a comprehensive understanding of outdoor 3D scenes. The central insight of our LiDAR-LLM is the reformulation of 3D outdoor scene cognition as a language modeling problem, encompassing tasks such as 3D captioning, 3D grounding, 3D question answering, etc. Specifically, due to the scarcity of 3D LiDAR-text pairing data, we introduce a three-stage training strategy and generate relevant datasets, progressively aligning the 3D modality with the language embedding of LLM. Furthermore, we design a Position-Aware Transformer (PAT) to connect the 3D encoder with the LLM, which effectively bridges the modality gap and enhances the LLM's spatial orientation comprehension of visual features. Our experiments demonstrate that LiDAR-LLM effectively comprehends a wide range of instructions related to 3D scenes, achieving a 40.9 BLEU-1 score on the 3D captioning dataset, a Grounded Captioning accuracy of 63.1%, and a BEV mIoU of 14.3%.

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AAAI Conference 2025 Conference Paper

SCALM: Detecting Bad Practices in Smart Contracts Through LLMs

Zongwei Li
Xiaoqi Li
Wenkai Li
Xin Wang

As the Ethereum platform continues to mature and gain widespread usage, it is crucial to maintain high standards of smart contract writing practices. While bad practices in smart contracts may not directly lead to security issues, they do elevate the risk of encountering problems. Therefore, to understand and avoid these bad practices, this paper introduces the first systematic study of bad practices in smart contracts, delving into over 35 specific issues. Specifically, we propose a large language models (LLMs)-based framework, SCALM. It combines Step-Back Prompting and Retrieval-Augmented Generation (RAG) to effectively identify and address various bad practices. Our extensive experiments using multiple LLMs and datasets have shown that SCALM outperforms existing tools in detecting bad practices in smart contracts.

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AAAI Conference 2024 Conference Paper

Exploring Sparse Visual Prompt for Domain Adaptive Dense Prediction

Senqiao Yang
Jiarui Wu
Jiaming Liu
Xiaoqi Li
Qizhe Zhang
Mingjie Pan
Yulu Gan
Zehui Chen

The visual prompts have provided an efficient manner in addressing visual cross-domain problems. Previous works introduce domain prompts to tackle the classification Test-Time Adaptation (TTA) problem by placing image-level prompts on the input and fine-tuning prompts for each target domain. However, since the image-level prompts mask out continuous spatial details in the prompt-allocated region, it will suffer from inaccurate contextual information and limited domain knowledge extraction, particularly when dealing with dense prediction TTA problems. To overcome these challenges, we propose a novel Sparse Visual Domain Prompts (SVDP) approach, which applies minimal trainable parameters (e.g., 0.1%) to pixels across the entire image and reserves more spatial information of the input. To better apply SVDP in extracting domain-specific knowledge, we introduce the Domain Prompt Placement (DPP) method to adaptively allocates trainable parameters of SVDP on the pixels with large distribution shifts. Furthermore, recognizing that each target domain sample exhibits a unique domain shift, we design Domain Prompt Updating (DPU) strategy to optimize prompt parameters differently for each sample, facilitating efficient adaptation to the target domain. Extensive experiments were conducted on widely-used TTA and continual TTA benchmarks, and our proposed method achieves state-of-the-art performance in both semantic segmentation and depth estimation tasks.

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NeurIPS Conference 2024 Conference Paper

RoboMamba: Efficient Vision-Language-Action Model for Robotic Reasoning and Manipulation

Jiaming Liu
Mengzhen Liu
Zhenyu Wang
Pengju An
Xiaoqi Li
Kaichen Zhou
Senqiao Yang
Renrui Zhang

A fundamental objective in robot manipulation is to enable models to comprehend visual scenes and execute actions. Although existing Vision-Language-Action (VLA) models for robots can handle a range of basic tasks, they still face challenges in two areas: (1) insufficient reasoning ability to tackle complex tasks, and (2) high computational costs for VLA model fine-tuning and inference. The recently proposed state space model (SSM) known as Mamba demonstrates promising capabilities in non-trivial sequence modeling with linear inference complexity. Inspired by this, we introduce RoboMamba, an end-to-end robotic VLA model that leverages Mamba to deliver both robotic reasoning and action capabilities, while maintaining efficient fine-tuning and inference. Specifically, we first integrate the vision encoder with Mamba, aligning visual tokens with language embedding through co-training, empowering our model with visual common sense and robotic-related reasoning. To further equip RoboMamba with SE(3) pose prediction abilities, we explore an efficient fine-tuning strategy with a simple policy head. We find that once RoboMamba possesses sufficient reasoning capability, it can acquire manipulation skills with minimal fine-tuning parameters (0. 1\% of the model) and time. In experiments, RoboMamba demonstrates outstanding reasoning capabilities on general and robotic evaluation benchmarks. Meanwhile, our model showcases impressive pose prediction results in both simulation and real-world experiments, achieving inference speeds 3 times faster than existing VLA models.

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NeurIPS Conference 2023 Conference Paper

Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation

Hongcheng Wang
Andy Guan Hong Chen
Xiaoqi Li
Mingdong Wu
Hao Dong

The task of Visual Object Navigation (VON) involves an agent's ability to locate a particular object within a given scene. To successfully accomplish the VON task, two essential conditions must be fulfiled: 1) the user knows the name of the desired object; and 2) the user-specified object actually is present within the scene. To meet these conditions, a simulator can incorporate predefined object names and positions into the metadata of the scene. However, in real-world scenarios, it is often challenging to ensure that these conditions are always met. Humans in an unfamiliar environment may not know which objects are present in the scene, or they may mistakenly specify an object that is not actually present. Nevertheless, despite these challenges, humans may still have a demand for an object, which could potentially be fulfilled by other objects present within the scene in an equivalent manner. Hence, this paper proposes Demand-driven Navigation (DDN), which leverages the user's demand as the task instruction and prompts the agent to find an object which matches the specified demand. DDN aims to relax the stringent conditions of VON by focusing on fulfilling the user's demand rather than relying solely on specified object names. This paper proposes a method of acquiring textual attribute features of objects by extracting common sense knowledge from a large language model (LLM). These textual attribute features are subsequently aligned with visual attribute features using Contrastive Language-Image Pre-training (CLIP). Incorporating the visual attribute features as prior knowledge, enhances the navigation process. Experiments on AI2Thor with the ProcThor dataset demonstrate that the visual attribute features improve the agent's navigation performance and outperform the baseline methods commonly used in the VON and VLN task and methods with LLMs. The codes and demonstrations can be viewed at https: //sites. google. com/view/demand-driven-navigation.

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YNIMG Journal 2010 Journal Article

Diffuse damage in pediatric traumatic brain injury: A comparison of automated versus operator-controlled quantification methods

Erin D. Bigler
Tracy J. Abildskov
Elisabeth A. Wilde
Stephen R. McCauley
Xiaoqi Li
Tricia L. Merkley
Michael A. Fearing
Mary R. Newsome

This investigation had two main objectives: 1) to assess the comparability of volumes determined by operator-controlled image quantification with automated image analysis in evaluating atrophic brain changes related to traumatic brain injury (TBI) in children, and 2) to assess the extent of diffuse structural changes throughout the brain as determined by reduced volume of a brain structure or region of interest (ROI). Operator-controlled methods used ANALYZE® software for segmentation and tracing routines of pre-defined brain structures and ROIs. For automated image analyses, the open-access FreeSurfer program was used. Sixteen children with moderate-to-severe TBI were compared to individually matched, typically developing control children and the volumes of 18 brain structures and/or ROIs were compared between the two methods. Both methods detected atrophic changes but differed in the magnitude of the atrophic effect with the best agreement in subcortical structures. The volumes of all brain structures/ROIs were smaller in the TBI group regardless of method used; overall effect size differences were minimal for caudate and putamen but moderate to large for all other measures. This is reflective of the diffuse nature of TBI and its widespread impact on structural brain integrity, indicating that both FreeSurfer and operator-controlled methods can reliably assess cross-sectional volumetric changes in pediatric TBI.

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