Author name cluster

Anjun Chen

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.

8 papers

2 author rows

AAAI Conference 2026 Conference Paper

Improving Sparse IMU-based Motion Capture with Motion Label Smoothing

Zhaorui Meng
Lu Yin
Yangqing Hou
Anjun Chen
Shihui Guo
Yipeng Qin

Sparse Inertial Measurement Units (IMUs) based human motion capture has gained significant momentum, driven by the adaptation of fundamental AI tools such as recurrent neural networks (RNNs) and transformers that are tailored for temporal and spatial modeling. Despite these achievements, current research predominantly focuses on pipeline and architectural designs, with comparatively little attention given to regularization methods, highlighting a critical gap in developing a comprehensive AI toolkit for this task. To bridge this gap, we propose motion label smoothing, a novel method that adapts the classic label smoothing strategy from classification to the sparse IMU-based motion capture task. Specifically, we first demonstrate that a naive adaptation of label smoothing, including simply blending a uniform vector or a "uniform" motion representation (e.g., dataset-average motion or a canonical T-pose), is suboptimal; and argue that a proper adaptation requires increasing the entropy of the smoothed labels. Second, we conduct a thorough analysis of human motion labels, identifying three critical properties: 1) Temporal Smoothness, 2) Joint Correlation, and 3) Low-Frequency Dominance, and show that conventional approaches to entropy enhancement (e.g., blending Gaussian noise) are ineffective as they disrupt these properties. Finally, we propose the blend of a novel skeleton-based Perlin noise for motion label smoothing, designed to raise label entropy while satisfying motion properties. Extensive experiments applying our motion label smoothing to three state-of-the-art methods across four real-world IMU datasets demonstrate its effectiveness and robust generalization (plug-and-play) capability.

PDF Details DOI

ICRA Conference 2025 Conference Paper

TaskExp: Enhancing Generalization of Multi-Robot Exploration with Multi-Task Pre-Training

Shaohao Zhu
Yixian Zhao
Yang Xu 0042
Anjun Chen
Jiming Chen 0001
Jinming Xu 0002

We aim to develop a general multi-agent reinforcement learning (MARL) policy that enables a group of robots to efficiently explore large-scale, unknown environments with random pose initialization. Existing MARL-based multi-robot exploration methods face challenges in reliably mapping observations to actions in large-scale scenarios and lack of zero-shot generalization to unknown environments. To this end, we propose a generic multi-task pre-training algorithm (termed TaskExp) to enhance the generalization of learning-based policies. In particular, we design a decision-related task to guide the policy to focus on valuable subspaces of the action space, improving the reliability of policy mapping. Moreover, two perception-related tasks-Location Estimation and Map Prediction-are designed to enhance the zero-shot capability of the policy by guiding it to extract general invariant features from unknown environments. With TaskExp pre-training, our policy significantly outperforms state-of-the-art planning-based methods in large-scale scenarios and demonstrates strong zero-shot performance in unseen environments. Furthermore, TaskExp can also be easily integrated to improve the existing learning-based multi-robot exploration methods.

Details

IROS Conference 2025 Conference Paper

Temporal-Spatial Representation Fusion for Dexterous Manipulation Learning with Unpaired Visual-Action Data

Guwen Han
Zhengnan Sun
Qingtao Liu
Yu Cui
Anjun Chen
Huajin Chen
Rong Xiong
Jiming Chen 0001

Supervised behavioral cloning using robot visual-action data has been widely investigated in robot manipulation. However, these methods typically require simultaneous acquisition of visual and action data, which makes them difficult to utilize unpaired visual-action datasets: e. g. videos on Internet or action only data which has less privacy and security concerns. To take advantage of the action data without synchronized visual observation, we propose UnVALe, a novel dexterous robotic manipulation RL framework that utilizes action data without paired images to learn priors of human dexterous manipulation skills. Specifically, an LSTM-based network is designed to learn the temporal action prior by reconstructing the input trajectories, and a VAE network is designed to learn the spatial action prior by reconstructing the input action. Novel rewards are proposed to incorporate the priors into reinforcement learning, which encourages action output from RL polices to maintain low reconstruction errors in the LSTM and VAE networks. We perform extensive validation on three dexterous robot manipulation tasks. The experimental results show that UnVALe can effectively improve robot manipulation performance. Compared with existing visual pretraining methods, our method achieves a more than 30% increase in success rates.

Details

ICRA Conference 2025 Conference Paper

UpViTaL: Unpaired Visual-Tactile Self-Supervised Representation Learning for Dexterous Robotic Manipulation

Guwen Han
Qingtao Liu
Yu Cui
Anjun Chen
Jiming Chen 0001
Qi Ye 0001

Visual and tactile pretraining have been extensively studied in dexterous robot manipulation tasks. However, existing methods typically require the simultaneous acquisition of visual and tactile data, making it difficult to utilize low-cost, unpaired visual-tactile datasets. Moreover, these methods often rely on tactile sensors to provide input data for reinforcement learning (RL) during the physical deployment of robotic dexterous hands, which highly increases deployment costs. To address these challenges, we propose UpViTaL, an unpaired visualtactile self-supervised representation learning method for RLbased robot dexterous manipulation. Specifically, we collect low-cost unpaired visual and tactile datasets for manipulation skill learning using a camera and tactile gloves on three robot manipulation tasks. The temporal tactile self-supervised representation learning module of UpViTaL is used to explore efficient tactile representations from time-series tactile data. In parallel, the visual pretraining module of UpViTaL helps to extract efficient visual representations from visual data. In addition, we fuse unpaired visual-tactile representations through an RL reward mechanism, which does not require robotic dexterous hands tactile sensors for practical deployment. We validate our approach on three dexterous robot manipulation tasks. Experimental results demonstrate that UpViTaL can efficiently learn robot manipulation skills. Compared to existing approaches for visual pretraining, our method significantly improves the success rate by more than 30%.

Details

ICRA Conference 2024 Conference Paper

CAMInterHand: Cooperative Attention for Multi-View Interactive Hand Pose and Mesh Reconstruction

Guwen Han
Qi Ye 0001
Anjun Chen
Jiming Chen 0001

Interactive hand mesh reconstruction from singleview images poses a significant challenge with the severe occlusion and depth ambiguity inherent in interactive hand gestures. Recent approaches that employ probabilistic models and tokenpruned techniques have shown decent results in multi-view human body reconstruction. Nevertheless, these methods have not fully utilized multi-scale semantic information from multiview images and are not applicable in scenarios involving severe occlusion during dual-hand interactions. Simultaneously, current single-view methods independently reconstruct the left and right hands, which are ineffective in enhancing the interaction between both hands. To address these challenges, we propose CAMInterHand, a cooperative attention-based method for multi-view interactive hand pose and mesh reconstruction. Specifically, CAMInterHand extracts local pyramid features and global vertex features from multi-scale feature maps of multi-view images, enabling the exploration of rich local semantic information and facilitating effective feature alignment. Furthermore, CAMInterHand employs the cooperative attention fusion module to fuse all features from multi-view images, enhancing interactions among vertices of dual hands within global and local contexts. We conduct extensive experiments on the large-scale multi-view dataset InterHand2. 6M and CAMInterHand achieves a substantial performance improvement over existing methods for multi-view and single-view interactive hand reconstruction.

Details

ICRA Conference 2024 Conference Paper

InterRep: A Visual Interaction Representation for Robotic Grasping

Yu Cui
Qi Ye 0001
Qingtao Liu
Anjun Chen
Gaofeng Li
Jiming Chen 0001

Recently, pre-trained vision models have gained significant attention in motor control, showcasing impressive performance across diverse robotic learning tasks. While previous works predominantly concentrate on the significance of the pre-training phase, the equally important task of extracting more effective representations based on existing pre-trained visual models remains unexplored. To better leverage the representation capabilities of pre-trained models for robotic grasping, we propose InterRep, a novel interaction representation method that possesses not only the strengths of pre-trained models, known for their robustness in noisy environments and their proficiency in recognizing essential features, but also the capacity of capturing dynamic interaction details and local geometric features during the grasping process. Based on the novel representation, we introduce a deep reinforcement learning method to learn generalizable grasping policies. The experimental results demonstrate that our proposed representation outperforms the baselines in terms of both training speed and generalization. For the generalized grasping tasks with dexterous robotic hands, our method boasts a success rate nearly 20% higher than methods using the global features of the entire image from pre-trained models. In addition, our proposed representation method demonstrates promising performance when applied to a different robotic hand and task. It also exhibits excellent performance on real robots with a success rate of 70%.

Details

ICRA Conference 2024 Conference Paper

MAexp: A Generic Platform for RL-based Multi-Agent Exploration

Shaohao Zhu
Jiacheng Zhou
Anjun Chen
Mingming Bai
Jiming Chen 0001
Jinming Xu 0002

The sim-to-real gap poses a significant challenge in RL-based multi-agent exploration due to scene quantization and action discretization. Existing platforms suffer from the inefficiency in sampling and the lack of diversity in Multi-Agent Reinforcement Learning (MARL) algorithms across different scenarios, restraining their widespread applications. To fill these gaps, we propose MAexp, a generic platform for multi-agent exploration that integrates a broad range of state-of-the-art MARL algorithms and representative scenarios. Moreover, we employ point clouds to represent our exploration scenarios, leading to high-fidelity environment mapping and a sampling speed approximately 40 times faster than existing platforms. Furthermore, equipped with an attention-based Multi-Agent Target Generator and a Single-Agent Motion Planner, MAexp can work with arbitrary numbers of agents and accommodate various types of robots. Extensive experiments are conducted to establish the first benchmark featuring several high-performance MARL algorithms across typical scenarios for robots with continuous actions, which highlights the distinct strengths of each algorithm in different scenarios.

Details

ICRA Conference 2023 Conference Paper

ImmFusion: Robust mmWave-RGB Fusion for 3D Human Body Reconstruction in All Weather Conditions

Anjun Chen
Xiangyu Wang
Kun Shi 0003
Shaohao Zhu
Bin Fang
Yingfeng Chen
Jiming Chen 0001
Yuchi Huo

3D human reconstruction from RGB images achieves decent results in good weather conditions but degrades dramatically in rough weather. Complementary, mmWave radars have been employed to reconstruct 3D human joints and meshes in rough weather. However, combining RGB and mmWave signals for robust all-weather 3D human reconstruction is still an open challenge, given the sparse nature of mmWave and the vulnerability of RGB images. In this paper, we present ImmFusion, the first mmWave-RGB fusion solution to reconstruct 3D human bodies in all weather conditions robustly. Specifically, our ImmFusion consists of image and point backbones for token feature extraction and a Transformer module for token fusion. The image and point backbones refine global and local features from original data, and the Fusion Transformer Module aims for effective information fusion of two modalities by dynamically selecting informative tokens. Extensive experiments on a large-scale dataset, mmBody, captured in various environments demonstrate that ImmFusion can efficiently utilize the information of two modalities to achieve a robust 3D human body reconstruction in all weather conditions. In addition, our method's accuracy is significantly superior to that of state-of-the-art Transformer-based LiDAR-camera fusion methods.

Details