arXiv subjects

Haoqi Han

Publications and source records attributed to Haoqi Han.

5 recordsLinked to original sources

arXiv2026

High-Degree-of-Freedom Lightweight Bioinspired Leg for Enhanced Mobility in Small Robots

In microrobotics, enhancing locomotion capabilities by increasing the degrees of freedom (DoF) of leg mechanisms under severe spatial constraints remains a significant challenge. Inspired by insect locomotion, this paper presents a novel micro-scale parallel leg mechanism with four degrees of freedom, and systematically analyzes its mechanical design, electrical system, and kinematics. The design incorporates two spherical five-bar linkages to achieve spatial motion within a parallel four-bar configuration. Furthermore, a concentric design strategy is employed to simplify the analytical solution of the leg kinematics. Due to the parallel system architecture, all actuators are located on the main body, substantially reducing the equivalent inertia of moving parts compared to traditional high-DOF leg structures. The total mass of the system is only 18.9 g, with an end-effector output force of approximately 0.5 N and a workspace exceeding 22255 mm3. Experimental results demonstrate that the proposed single-leg mechanism achieves excellent motion flexibility, highlighting its potential for micro bio-inspired robotics.

cs.RO↗

arXiv2026

PhysiFlow: Physics-Aware Humanoid Whole-Body VLA via Multi-Brain Latent Flow Matching and Robust Tracking

In the domain of humanoid robot control, the fusion of Vision-Language-Action (VLA) with whole-body control is essential for semantically guided execution of real-world tasks. However, existing methods encounter challenges in terms of low VLA inference efficiency or an absence of effective semantic guidance for whole-body control, resulting in instability in dynamic limb-coordinated tasks. To bridge this gap, we present a semantic-motion intent guided, physics-aware multi-brain VLA framework for humanoid whole-body control. A series of experiments was conducted to evaluate the performance of the proposed framework. The experimental results demonstrated that the framework enabled reliable vision-language-guided full-body coordination for humanoid robots.

cs.RO↗

arXiv2026

PILOT: A Perceptive Integrated Low-level Controller for Loco-manipulation over Unstructured Scenes

Humanoid robots hold great potential for diverse interactions and daily service tasks within human-centered environments, necessitating controllers that seamlessly integrate precise locomotion with dexterous manipulation. However, most existing whole-body controllers lack exteroceptive awareness of the surrounding environment, rendering them insufficient for stable task execution in complex, unstructured scenarios.To address this challenge, we propose PILOT, a unified single-stage reinforcement learning (RL) framework tailored for perceptive loco-manipulation, which synergizes perceptive locomotion and expansive whole-body control within a single policy. To enhance terrain awareness and ensure precise foot placement, we design a cross-modal context encoder that fuses prediction-based proprioceptive features with attention-based perceptive representations. Furthermore, we introduce a Mixture-of-Experts (MoE) policy architecture to coordinate diverse motor skills, facilitating better specialization across distinct motion patterns. Extensive experiments in both simulation and on the physical Unitree G1 humanoid robot validate the efficacy of our framework. PILOT demonstrates superior stability, command tracking precision, and terrain traversability compared to existing baselines. These results highlight its potential to serve as a robust, foundational low-level controller for loco-manipulation in unstructured scenes.

cs.RO↗

arXiv2025

Development of a 15-Degree-of-Freedom Bionic Hand with Cable-Driven Transmission and Distributed Actuation

In robotic hand research, minimizing the number of actuators while maintaining human-hand-consistent dimensions and degrees of freedom constitutes a fundamental challenge. Drawing bio-inspiration from human hand kinematic configurations and muscle distribution strategies, this work proposes a novel 15-DoF dexterous robotic hand, with detailed analysis of its mechanical architecture, electrical system, and control system. The bionic hand employs a new tendon-driven mechanism, significantly reducing the number of motors required by traditional tendon-driven systems while enhancing motion performance and simplifying the mechanical structure. This design integrates five motors in the forearm to provide strong gripping force, while ten small motors are installed in the palm to support fine manipulation tasks. Additionally, a corresponding joint sensing and motor driving electrical system was developed to ensure efficient control and feedback. The entire system weighs only 1.4kg, combining lightweight and high-performance features. Through experiments, the bionic hand exhibited exceptional dexterity and robust grasping capabilities, demonstrating significant potential for robotic manipulation tasks.

cs.RO↗

arXiv2024

AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarking

We introduce the largest abdominal CT dataset (termed AbdomenAtlas) of 20,460 three-dimensional CT volumes sourced from 112 hospitals across diverse populations, geographies, and facilities. AbdomenAtlas provides 673K high-quality masks of anatomical structures in the abdominal region annotated by a team of 10 radiologists with the help of AI algorithms. We start by having expert radiologists manually annotate 22 anatomical structures in 5,246 CT volumes. Following this, a semi-automatic annotation procedure is performed for the remaining CT volumes, where radiologists revise the annotations predicted by AI, and in turn, AI improves its predictions by learning from revised annotations. Such a large-scale, detailed-annotated, and multi-center dataset is needed for two reasons. Firstly, AbdomenAtlas provides important resources for AI development at scale, branded as large pre-trained models, which can alleviate the annotation workload of expert radiologists to transfer to broader clinical applications. Secondly, AbdomenAtlas establishes a large-scale benchmark for evaluating AI algorithms -- the more data we use to test the algorithms, the better we can guarantee reliable performance in complex clinical scenarios. An ISBI & MICCAI challenge named BodyMaps: Towards 3D Atlas of Human Body was launched using a subset of our AbdomenAtlas, aiming to stimulate AI innovation and to benchmark segmentation accuracy, inference efficiency, and domain generalizability. We hope our AbdomenAtlas can set the stage for larger-scale clinical trials and offer exceptional opportunities to practitioners in the medical imaging community. Codes, models, and datasets are available at https://www.zongweiz.com/dataset

cs.CV↗