Explanipedia

Unified and Efficient Factor Graph Accelerator Design for Robotic Optimization Open

Qiang Liu, Yihao Hua, Yuhui Hao, Yu Bo, Shaoshan Liu , et al. · 2025

Despite extensive efforts, existing approaches to design accelerators for optimization-based robotic applications have limitations related to insufficient real-time performance and high energy consumption. Some methods focus on designing g…

EfficientNav: Towards On-Device Object-Goal Navigation with Navigation Map Caching and Retrieval Open

Zebin Yang, Sunjian Zheng, Tong Xie, Tianshi Xu, Yu Bo , et al. · 2025

Object-goal navigation (ObjNav) tasks an agent with navigating to the location of a specific object in an unseen environment. Embodied agents equipped with large language models (LLMs) and online constructed navigation maps can perform Obj…

In-Context Learning can Perform Continual Learning Like Humans Open

Liuwang Kang, Fan Wang, Shaoshan Liu, Hung-Chyun Chou, Chuan Lin , et al. · 2025

Large language models (LLMs) can adapt to new tasks via in-context learning (ICL) without parameter updates, making them powerful learning engines for fast adaptation. While extensive research has examined ICL as a few-shot learner, whethe…

In-Context Learning as General-Purpose Learning: a Comprehensive Survey and New Perspectives Open

Fan Wang, Yu Bo, Ping Shao, Zhiyuan Chen, Hung-Chyun Chou , et al. · 2025

IN-CONTEXT LEARNING AS GENERAL-PURPOSE LEARNING: A COMPREHENSIVE SURVEY AND NEW PERSPECTIVES Open

Fan Wang, Yu Bo, Ping Shao, Zhiyuan Chen, Hung-Chyun Chou , et al. · 2025

A Sparsity-Aware Autonomous Path Planning Accelerator with HW/SW Co-Design and Multi-Level Dataflow Optimization Open

Yifan Zhang, Xiaoyu Niu, Hongzheng Tian, Yanjun Zhang, Yu Bo , et al. · 2025

Path planning is a critical task for autonomous driving, aiming to generate smooth, collision-free, and feasible paths based on input perception and localization information. The planning task is both highly time-sensitive and computationa…

A Sparsity-Aware Autonomous Path Planning Accelerator with HW/SW Co-Design and Multi-Level Dataflow Optimization Open

Yifan Zhang, Xiaoyu Niu, Hongzheng Tian, Yanjun Zhang, Yu Bo , et al. · 2025

Path planning is critical for autonomous driving, generating smooth, collision-free, feasible paths based on perception and localization inputs. However, its computationally intensive nature poses significant challenges for resource-constr…

Dadu-Corki: Algorithm-Architecture Co-Design for Embodied AI-powered Robotic Manipulation Open

Yiyang Huang, Yuhui Hao, Yu Bo, Feng Yan, Yuxin Yang , et al. · 2025

Training Cross-Morphology Embodied AI Agents: From Practical Challenges to Theoretical Foundations Open

Shaoshan Liu, Fan Wang, Hongjun Zhou, Yuanfeng Wang · 2025

While theory and practice are often seen as separate domains, this article shows that theoretical insight is essential for overcoming real-world engineering barriers. We begin with a practical challenge: training a cross-morphology embodie…

ADDT -- A Digital Twin Framework for Proactive Safety Validation in Autonomous Driving Systems Open

Bo Yu, Chaoran Yuan, Zishen Wan, Jie Tang, Fadi Kurdahi , et al. · 2025

Autonomous driving systems continue to face safety-critical failures, often triggered by rare and unpredictable corner cases that evade conventional testing. We present the Autonomous Driving Digital Twin (ADDT) framework, a high-fidelity …

Effect of Telehealth Follow-up Consultation in Pediatric Acute Otitis Media - Shenzhen City, Guangdong Province, China, 2023-2024. Open

Yuzhang Huang, Shige Qi, Shaoshan Liu, Zhongying Pan, Brian Siu , et al. · 2025

Telehealth services can effectively bridge geographical healthcare disparities while optimizing pediatric care delivery systems.

Towards Large-Scale In-Context Reinforcement Learning by Meta-Training in Randomized Worlds Open

Fan Wang, Ping Shao, Yiming Zhang, Bo Yu, Shaoshan Liu , et al. · 2025

In-Context Reinforcement Learning (ICRL) enables agents to learn automatically and on-the-fly from their interactive experiences. However, a major challenge in scaling up ICRL is the lack of scalable task collections. To address this, we p…

DaDu-E: Rethinking the Role of Large Language Model in Robotic Computing Pipeline Open

Wenhao Sun, S. C. Hou, Zixuan Wang, Yu Bo, Shaoshan Liu , et al. · 2024

Performing complex tasks in open environments remains challenging for robots, even when using large language models (LLMs) as the core planner. Many LLM-based planners are inefficient due to their large number of parameters and prone to in…

Dataflow Accelerator Architecture for Autonomous Machine Computing Open

Shaoshan Liu, Yuhao Zhu, Yu Bo, Jean‐Luc Gaudiot, Guang R. Gao · 2024

Health satisfaction outcome from integrated autonomous mobile clinics Open

Yuzhang Huang, Shaoshan Liu, Zhongying Pan, C.H. Wu, Herng‐Chia Chiu , et al. · 2024

Autonomous mobile clinics (AMCs) have the potential to revolutionize healthcare delivery by bringing healthcare services to patients at the order of patient's fingertips. Particularly, AMCs can act as an essential touch point of integrated…

DaDu-Corki: Algorithm-Architecture Co-Design for Embodied AI-powered Robotic Manipulation Open

Yiyang Huang, Yuhui Hao, Bo Yu, Feng Yan, Yuxin Yang , et al. · 2024

Embodied AI robots have the potential to fundamentally improve the way human beings live and manufacture. Continued progress in the burgeoning field of using large language models to control robots depends critically on an efficient comput…

Shaping the Outlook for the Autonomy Economy Open

Shaoshan Liu · 2024

In each issue of Communications , we publish selected posts or excerpts from the many blogs on our website. The views expressed by bloggers are their own and not necessarily held by Communications or the Association for Computing Machinery…

ICE-SEARCH: A Language Model-Driven Feature Selection Approach Open

Tianze Yang, Tianyi Yang, Shaoshan Liu, Fuyuan Lvu, Xue Liu · 2024

This study unveils the In-Context Evolutionary Search (ICE-SEARCH) method, which is among the first works that melds large language models (LLMs) with evolutionary algorithms for feature selection (FS) tasks and demonstrates its effectiven…

AICOM-MP: an AI-based monkeypox detector for resource-constrained environments Open

T. Yang, Tianze Yang, Andrew Liu, Na An, Shaoshan Liu , et al. · 2024

Under the Autonomous Mobile Clinics (AMCs) initiative, the AI Clinics on Mobile (AICOM) project is developing, open sourcing, and standardising health AI technologies on low-end mobile devices to enable health-care access in least-develope…

AI Clinics on Mobile (AICOM): Universal AI Doctors for the Underserved and Hard-to-Reach Open

Tianyi Yang, Tianze Yang, Na An, Ao Kong, Jie Tang , et al. · 2024

This paper introduces Artificial Intelligence Clinics on Mobile (AICOM), an open-source project devoted to answering the United Nations Sustainable Development Goal 3 (SDG3) on health, which represents a universal recognition that health i…

AICOM-MP: an AI-based Monkeypox Detector for Resource-Constrained Environments Open

Tim Tianyi Yang, Tom Tianze Yang, Andy Liu, Jie Tang, Na An , et al. · 2023

Under the Autonomous Mobile Clinics (AMCs) initiative, the AI Clinics on Mobile (AICOM) project is developing, open sourcing, and standardizing health AI technologies on low-end mobile devices to enable healthcare access in least developed…

Timely Fusion of Surround Radar/Lidar for Object Detection in Autonomous Driving Systems Open

Wenjing Xie, Tao Hu, Neiwen Ling, Guoliang Xing, Shaoshan Liu , et al. · 2023

Fusing Radar and Lidar sensor data can fully utilize their complementary advantages and provide more accurate reconstruction of the surrounding for autonomous driving systems. Surround Radar/Lidar can provide 360-degree view sampling with …

Table of Contents Open

Pratheek S. Upadhyaya, Nishith Tripathi, Joseph Gaeddert, Jeffrey H. Reed, Shalli Rani , et al. · 2023

A Comprehensive Review and Systematic Analysis of Artificial Intelligence Regulation Policies Open

Weiyue Wu, Shaoshan Liu · 2023

Due to the cultural and governance differences of countries around the world, there currently exists a wide spectrum of AI regulation policy proposals that have created a chaos in the global AI regulatory space. Properly regulating AI tech…

Autonomy 2.0: The Quest for Economies of Scale Open

Shuang Wu, Bo Yu, Shaoshan Liu, Yuhao Zhu · 2023

With the advancement of robotics and AI technologies in the past decade, we have now entered the age of autonomous machines. In this new age of information technology, autonomous machines, such as service robots, autonomous drones, deliver…

AI Clinics on Mobile (AICOM): Universal AI Doctors for the Underserved and Hard-to-Reach Open

Tim Tianyi Yang, Tom Tianze Yang, Na An, Ao Kong, Shaoshan Liu , et al. · 2023

This paper introduces Artificial Intelligence Clinics on Mobile (AICOM), an open-source project devoted to answering the United Nations Sustainable Development Goal 3 (SDG3) on health, which represents a universal recognition that health i…

Compliance Costs of AI Technology Commercialization: A Field Deployment Perspective Open

Weiyue Wu, Shaoshan Liu · 2023

While Artificial Intelligence (AI) technologies are progressing fast, compliance costs have become a huge financial burden for AI startups, which are already constrained on research & development budgets. This situation creates a complianc…

Thales: Formulating and Estimating Architectural Vulnerability Factors for DNN Accelerators Open

Abhishek Tyagi, Yiming Gan, Shaoshan Liu, Bo Yu, Paul N. Whatmough , et al. · 2022

As Deep Neural Networks (DNNs) are increasingly deployed in safety critical and privacy sensitive applications such as autonomous driving and biometric authentication, it is critical to understand the fault-tolerance nature of DNNs. Prior …

AICOM-MP: an AI-based Monkeypox Detector for Resource-Constrained Environments Open

Tim Tianyi Yang, Tom Tianze Yang, Andy Liu, Jie Tang, Na An , et al. · 2022

Under the Autonomous Mobile Clinics (AMCs) initiative, we are developing, open sourcing, and standardizing health AI technologies to enable healthcare access in least developed countries (LDCs). We deem AMCs as the next generation of healt…

INTERNEURON: A Middleware with Multi-Network Communication Reliability for Infrastructure Vehicle Cooperative Autonomous Driving Open

Tianze Wu, Shaoshan Liu, Bo Yu, Sa Wang, Yungang Bao , et al. · 2022

Infrastructure-Vehicle Cooperative Autonomous Driving (IVCAD) is a new paradigm of autonomous driving, which relies on the cooperation between intelligent roads and autonomous vehicles. This paradigm has been shown to be safer and more eff…

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