Author name cluster

Junyan Li

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

2 author rows

AAAI Conference 2026 Conference Paper

FoAM: Foresight-Augmented Multi-Task Imitation Policy for Robotic Manipulation

Litao Liu
Wentao Wang
Yifan Han
Zhuoli Xie
Pengfei Yi
Junyan Li
Wenzhao Lian

Multi-task imitation learning (MTIL) has shown significant potential in robotic manipulation by enabling agents to perform various tasks using a single policy. It simplifies the policy deployment and enhances the agent's adaptability across different scenarios. However, key challenges remain, such as maintaining action reliability (e.g., avoiding abnormal action sequences that deviate from nominal task trajectories) and generalizing to unseen tasks with a few expert demonstrations. To address these challenges, we introduce the Foresight-Augmented Manipulation (FoAM) policy, a novel MTIL policy that pioneers the use of multi-modal goal conditions as input and introduces a foresight augmentation in addition to the general action reconstruction. FoAM enables the agent to reason about its actions' visual consequences (foresight) and to be guided by these more expressive representations during task execution. Extensive experiments on over 100 tasks in simulation and real-world settings demonstrate that FoAM significantly enhances MTIL policy performance, outperforming state-of-the-art baselines by up to 41% in success rate. We released our simulation suites that include over 80 challenging tasks across more than 10 scenarios designed for manipulation policy training and evaluation.

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ICML Conference 2025 Conference Paper

CommVQ: Commutative Vector Quantization for KV Cache Compression

Junyan Li
Yang Zhang 0001
Muhammad Yusuf Hassan
Talha Chafekar
Tianle Cai
Zhile Ren
Pengsheng Guo
Foroozan Karimzadeh

Large Language Models (LLMs) are increasingly used in applications requiring long context lengths, but the key-value (KV) cache often becomes a memory bottleneck on GPUs as context grows. To address this, we propose Commutative Vector Quantization (CommVQ) to significantly reduce memory usage for long-context LLM inference. We first introduce additive quantization with a lightweight encoder and codebook to compress the KV cache, which can be decoded via simple matrix multiplication. To further reduce computational costs during decoding, we design the codebook to be commutative with Rotary Position Embedding (RoPE) and train it using an Expectation-Maximization (EM) algorithm. This enables efficient integration of decoding into the self-attention mechanism. Our approach achieves high accuracy with additive quantization and low overhead via the RoPE-commutative codebook. Experiments on long-context benchmarks and GSM8K show that our method reduces FP16 KV cache size by 87. 5% with 2-bit quantization, while outperforming state-of-the-art KV cache quantization methods. Notably, it enables 1-bit KV cache quantization with minimal accuracy loss, allowing a LLaMA-3. 1 8B model to run with a 128K context length on a single RTX 4090 GPU. The source code is available at: https: //github. com/UMass-Embodied-AGI/CommVQ.

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IROS Conference 2025 Conference Paper

Splatter Joint: 3D Gaussian Splatting for Articulated Objects

Junyan Li
Yifan Han
Pengfei Yi
Wenzhao Lian

3D reconstruction methods such as 3D Gaussian Splatting (3DGS), have achieved significant advancements in recent years. However, the study of articulated objects remains limited due to their geometric and dynamic complexity. We propose Splatter Joint, a novel method that models articulated objects, particularly focusing on joints, to capture both the appearance and the geometric information from a few images taken at a single viewpoint. By integrating joint parameters into the 3DGS rendering process in a differentiable manner, we enable the prediction of joint movements while enhancing the accuracy of object appearance reconstruction. We evaluated Splatter Joint on existing and newly created datasets, demonstrating its effectiveness in modeling object appearance and geometry simultaneously.

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ICLR Conference 2024 Conference Paper

CoVLM: Composing Visual Entities and Relationships in Large Language Models Via Communicative Decoding

Junyan Li
Delin Chen
Yining Hong
Zhenfang Chen
Peihao Chen
Yikang Shen
Chuang Gan 0001

A remarkable ability of human beings resides in compositional reasoning, i.e., the capacity to make "infinite use of finite means". However, current large vision-language foundation models (VLMs) fall short of such compositional abilities due to their ``bag-of-words" behaviors and inability to construct words that correctly represent visual entities and the relations among the entities. To this end, we propose CoVLM, which can guide the LLM to explicitly compose visual entities and relationships among the text and dynamically communicate with the vision encoder and detection network to achieve vision-language communicative decoding. Specifically, we first devise a set of novel communication tokens for the LLM, for dynamic communication between the visual detection system and the language system. A communication token is generated by the LLM following a visual entity or a relation, to inform the detection network to propose regions that are relevant to the sentence generated so far. The proposed regions-of-interests (ROIs) are then fed back into the LLM for better language generation contingent on the relevant regions. The LLM is thus able to compose the visual entities and relationships through the communication tokens. The vision-to-language and language-to-vision communication are iteratively performed until the entire sentence is generated. Our framework seamlessly bridges the gap between visual perception and LLMs and outperforms previous VLMs by a large margin on compositional reasoning benchmarks (e.g., ~20% in HICO-DET mAP, ~14% in Cola top-1 accuracy, and ~3% on ARO top-1 accuracy). We also achieve state-of-the-art performances on traditional vision-language tasks such as referring expression comprehension and visual question answering.

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JBHI Journal 2021 Journal Article

Heartbeats Classification Using Hybrid Time-Frequency Analysis and Transfer Learning Based on ResNet

Yatao Zhang
Junyan Li
Shoushui Wei
Fengyu Zhou
Dong Li

The classification of heartbeats is an important method for cardiac arrhythmia analysis. This study proposes a novel heartbeat classification method using hybrid time-frequency analysis and transfer learning based on ResNet-101. The proposed method has the following major advantages over the afore-mentioned methods: it avoids the need for manual features extraction in the traditional machine learning method, and it utilizes 2-D time-frequency diagrams which provide not only frequency and energy information but also preserve the morphological characteristic within the ECG recordings, and it owns enough deep to make better use of performance of CNN. The method deploys a hybrid time-frequency analysis of the Hilbert transform (HT) and the Wigner-Ville distribution (WVD) to transform 1-D ECG recordings into 2-D time-frequency diagrams which were then fed into a transfer learning classifier based on ResNet-101 for two classification tasks (i. e. , 5 heartbeat categories assigned by the ANSI/AAMI standard (i. e. , N, V, S, Q and F) and 14 original beat kinds of the MIT/BIH arrhythmia database). For 5 heartbeat categories classification, the results show the F1-score of N, V, S, Q and F categories are F $_{N}$ 0. 9899, F $_{V}$ 0. 9845, F $_{S}$ 0. 9376, F $_{Q}$ 0. 9968, F $_{F}$ 0. 8889, respectively, and the overall F1-score is 0. 9595 using the combination data balancing. The results show the average values for accuracy, sensitivity, specificity, predictive value and F1-score on test set for 14 beat kinds the MIT-BIH arrhythmia database are 99. 75%, 91. 36%, 99. 85%, 90. 81% and 0. 9016, respectively. Compared with other methods, the proposed method can yield more accurate results.

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