Xieyuanli Chen

Developing an amphibious hip powered exoskeleton (AHPE) for lower limb augment in amphibious environments is crucial, which is facing the challenges, such as mechanical design, effective human-exoskeleton interaction, and simulating scenarios, for performance validations. To tackle these issues, this work proposes a design of a cable-driven AHPE named AmphiHip Exo for the hip flexion/extension assisting. To achieve an effective human-exoskeleton interaction, a physics-informed neural network named PILaN is proposed to realize hip motion intention perception (HMIP)-based control of the AmphiHip Exo. Trained by a self-collected small-scale dataset containing hip states of human activities, the proposed PILaN can automatically proceed HMIP, including joint trajectory and velocity estimations, with real-time dynamics parameter estimations and the Lagrangian dynamics model. Prototype tests are conducted on various wearers under four designed task scenarios: daily handling, climbing stairs, underwater gait assistance, and water rescue, and the performances are validated according to our designed evaluation metrics: HMIP accuracy, power assisting effect (PAE), and mechanical reliability. Experimental results prove the accuracy and efficiency of PILaN, the PAE among different wearers and task scenarios, and the waterproof effect as well as battery endurance, demonstrating the feasibility of the designed AmphiHip Exo.

Multi-Layer Perceptron (MLP) models are the foundation of contemporary point cloud processing. However, their complex network architectures obscure the source of their strength and limit the application of these models. In this article, we develop a two-stage abstraction and refinement (ABS-REF) view for modular feature extraction in point cloud processing. This view elucidates that whereas the early models focused on ABS stages, the more recent techniques devise sophisticated REF stages to attain performance advantages. Then, we propose a High-dimensional Positional Encoding (HPE) module to explicitly utilize intrinsic positional information, extending the "positional encoding" concept from Transformer literature. HPE can be readily deployed in MLP-based architectures and is compatible with transformer-based methods. Within our ABS-REF view, we rethink local aggregation in MLP-based methods and propose replacing time-consuming local MLP operations, which are used to capture local relationships among neighbors. Instead, we use non-local MLPs for efficient non-local information updates, combined with the proposed HPE for effective local information representation. We leverage our modules to develop HPENets, a suite of MLP networks that follow the ABS-REF paradigm, incorporating a scalable HPE-based REF stage. Extensive experiments on seven public datasets across four different tasks show that HPENets deliver a strong balance between efficiency and effectiveness. Notably, HPENet surpasses PointNeXt, a strong MLP-based counterpart, by 1.1% mAcc, 4.0% mIoU, 1.8% mIoU and 0.2% Cls. mIoU, with only 50.0%, 21.5%, 23.1%, 44.4% of FLOPs on ScanObjectNN, S3DIS, ScanNet, and ShapeNetPart, respectively.

Forecasting how human hands move in egocentric views is critical for applications like augmented reality, human-robot policy transfer, and service/assistive technologies. Recently, several hand trajectory prediction (HTP) methods have been developed to generate future possible hand waypoints, which still suffer from insufficient prediction targets, inherent modality gaps, entangled hand-head motion, and limited validation in downstream tasks. To address these limitations, we present Uni-Hand, a universal hand motion forecasting framework considering multi-modal input, multi-dimensional and multi-target prediction patterns, and multi-task affordances for downstream applications. We harmonize multiple modalities by vision-language fusion, global context incorporation, and task-aware text embedding injection, to forecast hand waypoints in both 2D and 3D spaces. A novel dual-branch diffusion is proposed to concurrently predict human head and hand movements, capturing their motion synergy in egocentric vision. By introducing target indicators, the prediction model can forecast the specific joint waypoints of the wrist or the fingers, besides the widely studied hand center points. In addition, we enable Uni-Hand to additionally predict hand-object interaction states (contact/separation) to facilitate downstream tasks better. To incorporate comprehensive downstream task evaluations in the literature, we build novel benchmarks to assess the real-world applicability of hand motion forecasting algorithms. The experimental results on multiple publicly available datasets and our newly proposed benchmarks demonstrate that Uni-Hand achieves the state-of-the-art performance in multi-dimensional and multi-target hand motion forecasting. Extensive validation in multiple downstream tasks also presents its impressive human-robot policy transfer to enable robotic manipulation, and effective feature enhancement for action anticipation/recognition.

LiDAR-based place recognition (LPR) is a key component of visual localization and autonomous driving. Although LiDAR data are usually preprocessed by motion undistortion, which can greatly reduce scene distortion caused by sensor motion, 3-dimensional (3D) point clouds in complex scenes still show inherent unstructured sparsity. Projecting 3D point clouds into 2D range view (RV) images can provide structured feature representations that are more suitable for neural networks and deep learning methods. However, this strategy has two main limitations: 1) the important geometric details are removed by the projection, impairing the effective extraction and representation of scene features. 2) unpredictable occlusions and viewpoint changes alter the observed scene structure and often lead to incorrect place matches. To address these limitations, we propose a LPR network that combines topological information extracted by persistent homology (PH) with RV features to compensate for missing structural information and produce more stable descriptors. Specifically, PH-stable features are added as a topology-aware sampling channel for the RV feature to integrate more stable and discriminative robust geometric cues. Next, we introduce a spatiotemporal fusion module that links topological and RV features across consecutive frames, thereby improving spatiotemporal consistency of the representation and thus reducing the impact of occlusions and rotations. Compared with OverlapMamba, PHMRNet can better handle spatial, temporal, and geometric features, improving the robustness of the descriptor. Experiments on public datasets show that PHMRNet outperforms existing methods in terms of accuracy and robustness under the same driving conditions and LiDAR scanning rate.