The new consumer‑grade robot from VeilBlue delivers a thousand‑fold compute boost over previous models, matching Nvidia's Jetson AGX Thor while costing only one‑tenth, thanks to a six‑chip heterogeneous edge cluster, human‑surpassing perception, and safety‑first design validated in real homes.
This article breaks down an AI agent's workflow—perception, planning, action, and memory—using a product‑launch example, explains reasoning methods like Chain‑of‑Thought and ReAct, details tool integration, memory types, common failure modes, and why planning and tool ecosystems are essential.
This guide walks through setting up a PAI‑DSW environment, downloading Isaac Sim assets, configuring noVNC, launching a software‑in‑the‑loop robot simulation, and running a perception pipeline that combines FastSAM detection with FoundationPose pose estimation and ICP refinement.
This article explains what an AI agent is, outlines its four core modules—perception, memory, planning, and action—describes the role of large language models, compares software development generations, discusses memory implementations, planning methods like ReAct and Plan‑and‑Solve, and covers evaluation, cost analysis, and differences between agents and workflows.
This article shares practical experience training perception‑detection mini‑models on two different compute cards, covering environment setup, technical architecture, common dependency issues, performance‑boosting tricks such as CPU process pools, torch dataloader tuning, NCCL P2P handling, and CPFS storage optimization.
This article provides a comprehensive overview of AI Agents, tracing their evolution from traditional chatbots to LLM‑driven agents, explaining core components such as perception, reasoning, action, knowledge bases, learning and communication interfaces, and discussing practical use cases, interaction cycles, and future prospects.
This article introduces embodied intelligence, explains its basic definition, three essential elements (body, intelligence, environment), and details the four functional modules—perception, decision, action, and feedback—while describing the sensors and algorithms that enable physical AI systems to interact with the real world.
Tencent’s Robotics X lab unveils a versatile dual‑arm humanoid framework that integrates vision‑based optimal grasping, safety‑ensured collaborative planning, and skill‑transfer interfaces, enabling tasks such as synchronized screw turning, pouring, interference suppression, and large‑object handovers, as detailed in a recent IEEE Transactions on Robotics paper.
Baidu unveiled Apollo Open Platform 9.0 on December 19, delivering Package Management 2.0, new perception models with 4D millimeter‑wave radar support, incremental training, and a revamped Dreamview+ UI, and will host two live‑stream courses on December 27‑28 to explain the PnC and perception upgrades, with gifts for registrants.
By fusing LiDAR point clouds and camera images into a unified bird‑eye‑view space and applying Transformer‑based perception, multi‑sensor fusion, and graph‑diffusion modeling, the proposed BEV+Transformer framework automatically detects and smooths ground‑level line features and signs for high‑definition maps with centimeter‑level accuracy, boosting production efficiency and reducing cost.
The information processing theory, emerging in the 1950s‑60s, likens human cognition to computer operations, detailing how perception, attention, memory, conceptual knowledge, reasoning, and feedback mechanisms transform sensory input into mental representations and guide behavior, influencing cognitive psychology, education, and HCI.
The article proposes a three‑layer, data‑driven framework—problem analysis using massive fleet data, iterative deep‑learning algorithm development with fallback and explainable‑AI safeguards, and systematic validation via simulation and real‑world tests—to mitigate perception, prediction, and control uncertainties and advance trustworthy autonomous‑driving control systems.
The article traces L4 autonomous-driving perception from early rule-based point-cloud methods through data-driven deep-learning models to emerging self-learning, multi-task systems, and highlights four key hurdles—model generalization and explainability, robust multi-sensor fusion, real-time compute limits, and proper uncertainty handling—calling for integrated AI, engineering, and data solutions.
This article reviews perception technologies used in autonomous heavy‑truck systems—including lane‑line detection, obstacle detection, and LiDAR sensing—detailing traditional and deep‑learning approaches, practical challenges on high‑speed highways, and the cost, performance, and reliability issues faced when moving these solutions to mass production.
This article outlines Pony.ai's perception pipeline, comparing traditional and deep‑learning approaches for 2D and 3D object detection, detailing sensor fusion, detection methods, challenges such as occlusion and distance estimation, and how hybrid techniques improve accuracy for autonomous driving.
This article presents a comprehensive overview of autonomous driving perception, covering system fundamentals, sensor setups and fusion techniques, onboard processing architecture, and the key technical challenges such as precision‑recall balance, adverse weather, and small‑object detection.
This article provides a detailed overview of autonomous driving, covering its evolution from electric and shared vehicles, major industry players, technical definitions, SAE level classifications, core modules such as perception, localization, decision and control, key datasets like KITTI, and emerging business opportunities in the sector.
This reflective essay explores Kenya’s design philosophy, examining how perception, memory, and the concept of emptiness shape meaningful, user‑centered creations—from MU‑JI’s minimalist branding to tactile exhibitions—offering insights for designers seeking deeper purpose beyond mere aesthetics.
