{"product_id":"deep-robotics-lite3-pro","title":"Deep Robotics Lite3 Pro","description":"\u003c!-- PRODUCT: Deep Robotics Lite3 Pro --\u003e\n\u003ch2 style=\"text-align: center;\"\u003eDeep Robotics Lite3 Pro\u003c\/h2\u003e\n\u003cdiv style=\"font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; line-height: 1.6; color: #333;\"\u003e\n\u003ch3\u003eOverview\u003c\/h3\u003e\n\u003cdiv style=\"display: flex; gap: 30px; align-items: flex-start; flex-wrap: wrap;\"\u003e\n\u003cdiv style=\"flex: 1; order: 1; min-width: 300px;\"\u003e\n\u003cp\u003eThe Deep Robotics Lite3 Pro is a research-grade quadruped platform that bridges exceptional locomotion capabilities with sophisticated AI perception. Featuring the proven RK3588 motion controller paired with an NVIDIA Jetson Xavier NX processor and Intel RealSense D435i depth camera, the Lite3 Pro delivers advanced perception without the complexity of full LiDAR integration. This configuration makes it ideal for researchers focusing on visual SLAM, neural network inference, deep learning-based perception, and AI-driven autonomous behaviors. The split-brain architecture keeps motion control independent while enabling full experimental freedom on the perception side.\u003c\/p\u003e\n\u003cp\u003eThe split-brain architecture with independent motion controller and perception processor enables researchers to modify AI algorithms without affecting the stability and reliability of locomotion control. This separation is critical for developing and testing new neural network models without risking robot stability.\u003c\/p\u003e\n\u003cp\u003eThe Intel RealSense D435i depth camera and NVIDIA Jetson Xavier NX provide professional-grade perception capabilities for visual SLAM, object detection, and semantic understanding tasks. This configuration optimizes cost-to-capability ratio for vision-intensive research applications.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv style=\"flex: 0 0 400px; order: 2;\"\u003e\u003cimg style=\"width: 100%; height: auto;\" alt=\"Deep Robotics Lite3 Pro research-grade platform\" src=\"https:\/\/image2url.com\/r2\/default\/images\/1773879051843-ac8c2488-d050-47d1-8713-788bb58695c0.jpg\"\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003chr\u003e\n\u003ch3\u003eTechnical Specifications\u003c\/h3\u003e\n\u003cdiv style=\"display: flex; gap: 30px; align-items: flex-start; flex-wrap: wrap;\"\u003e\n\u003cdiv style=\"flex: 0 0 500px; order: 1; display: flex; flex-direction: column; gap: 20px;\"\u003e\u003cimg style=\"width: 100%; height: auto;\" alt=\"Lite3 Pro dual-processor architecture diagram\" src=\"https:\/\/image2url.com\/r2\/default\/images\/1773879363327-67ac7561-7f71-4610-ba70-53120be24ea5.jpg\"\u003e\u003c\/div\u003e\n\u003cdiv style=\"flex: 1; order: 2; min-width: 300px;\"\u003e\n\u003cul style=\"list-style-type: disc; padding-left: 0; margin-left: 0; list-style-position: inside;\"\u003e\n\u003cli\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 610 × 370 × 445 mm (Standing)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eWeight:\u003c\/strong\u003e 12.2–13.5 kg (including battery, sensor package)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePayload Capacity:\u003c\/strong\u003e 5 kg continuous; 7.5 kg maximum\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBattery Endurance:\u003c\/strong\u003e 1.5–2 hours continuous operation\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOperating Range:\u003c\/strong\u003e 4 km (with perception host)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMax Speed:\u003c\/strong\u003e 4 m\/s\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSlope Climbing:\u003c\/strong\u003e Up to 40°\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eStair Climbing:\u003c\/strong\u003e 15 cm continuous steps\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMotion Host Processor:\u003c\/strong\u003e RK3588 ARM-architecture (1 kHz control frequency)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePerception Processor:\u003c\/strong\u003e NVIDIA Jetson Xavier NX (8-core ARM A72, 8GB LPDDR4)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDepth Camera:\u003c\/strong\u003e Intel RealSense D435i RGB-D sensor\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eJoint Torque:\u003c\/strong\u003e 50% higher than previous generation\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eVisual SLAM Capability:\u003c\/strong\u003e Real-time Visual SLAM using RealSense depth stream\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eNeural Network Inference:\u003c\/strong\u003e CUDA-enabled TensorFlow and PyTorch support\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eObject Detection:\u003c\/strong\u003e YOLOv8 and other DNN models deployable on Jetson NX\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOperating System:\u003c\/strong\u003e Ubuntu 20.04 (ROS1 and ROS2 compatible)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eROS Versions:\u003c\/strong\u003e ROS1 (Noetic) and ROS2 (Foxy) with switching support\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDevelopment Access:\u003c\/strong\u003e Full open SDK and API access\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eArchitecture Design:\u003c\/strong\u003e Split-brain (motion\/perception) for research independence\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePositioning:\u003c\/strong\u003e Mid-high tier variant with balanced perception and locomotion\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGPU Architecture:\u003c\/strong\u003e NVIDIA Ampere with 1024 CUDA cores for parallel AI workloads\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDepth Camera Count:\u003c\/strong\u003e Dual Intel RealSense units providing stereo depth perception\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCharging Time:\u003c\/strong\u003e Approximately 1.5 hours from depleted to full charge\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBody Material:\u003c\/strong\u003e High-strength composite chassis with impact-resistant panels and dual camera protection\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003chr\u003e\n\u003ch3\u003eKey Features\u003c\/h3\u003e\n\u003cdiv style=\"display: flex; gap: 30px; align-items: flex-start; flex-wrap: wrap;\"\u003e\n\u003cdiv style=\"flex: 1; order: 1; min-width: 300px;\"\u003e\n\u003cul style=\"list-style-type: disc; padding-left: 0; margin-left: 0; list-style-position: inside;\"\u003e\n\u003cli\u003e\n\u003cstrong\u003eDual-Processor Split-Brain:\u003c\/strong\u003e RK3588 handles motion control; Jetson Xavier NX handles AI perception independently\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eNVIDIA Jetson Xavier NX:\u003c\/strong\u003e 8-core ARM A72 processor with dedicated GPU for neural network inference\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eIntel RealSense D435i:\u003c\/strong\u003e RGB-D depth camera for visual perception and localization\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eVisual SLAM Support:\u003c\/strong\u003e Real-time ORB-SLAM or similar algorithms on depth stream\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDeep Learning Ready:\u003c\/strong\u003e CUDA-enabled TensorFlow and PyTorch for on-robot neural networks\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eObject Detection:\u003c\/strong\u003e Run YOLOv8, MobileNet, and other DNN models for visual understanding\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e1 kHz Motion Control:\u003c\/strong\u003e Real-time joint control independent of perception processing\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eReinforcement Learning Support:\u003c\/strong\u003e Train and deploy RL policies via Lite3_rl_deploy\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eROS1 and ROS2 Dual Support:\u003c\/strong\u003e Flexible software framework compatibility with built-in switching\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eEnhanced Joint Torque:\u003c\/strong\u003e 50% increased drive torque for improved agility and payload\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eResearch Independence:\u003c\/strong\u003e Motion stability isolated from perception experiments\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eExtended Connectivity:\u003c\/strong\u003e Ethernet connectivity for streaming and network communication\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAI-Optimized Gaits:\u003c\/strong\u003e Pre-loaded RL-trained locomotion algorithms for improved stability\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eModular Sensor Architecture:\u003c\/strong\u003e Support for additional sensors and perception modules\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eEnterprise Research Grade:\u003c\/strong\u003e Industrial-strength specifications for academic and commercial research\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSim-to-Real Pipeline:\u003c\/strong\u003e Support for training in simulation and deploying to hardware\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAPI for Custom Applications:\u003c\/strong\u003e Full SDK access for developing perception-driven behaviors\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMulti-Framework Compatibility:\u003c\/strong\u003e Supports multiple robotics and AI development frameworks\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eProfessional Support:\u003c\/strong\u003e Access to dedicated technical resources and research community\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eExtensible Design:\u003c\/strong\u003e Open architecture supports future capability additions and upgrades\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e1024 CUDA Cores:\u003c\/strong\u003e Massive parallel processing power for real-time neural network inference and computer vision\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eStereo Depth Perception:\u003c\/strong\u003e Dual RealSense cameras provide overlapping fields of view for robust 3D environment mapping\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHot-Swappable Battery:\u003c\/strong\u003e Quick-release battery mechanism enables field replacement without tools\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSimultaneous AI Pipelines:\u003c\/strong\u003e GPU headroom supports running perception, planning, and control networks concurrently\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003cdiv style=\"flex: 0 0 500px; order: 2; display: flex; flex-direction: column; gap: 20px;\"\u003e\u003cimg style=\"width: 100%; height: auto;\" alt=\"Lite3 Pro perception and AI capabilities demonstration\" src=\"https:\/\/www.image2url.com\/r2\/default\/images\/1776206263629-eb8eb999-94c9-4a94-b839-5d797673e231.jpg\"\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003chr\u003e\n\u003ch3\u003eApplications\u003c\/h3\u003e\n\u003cdiv style=\"display: flex; gap: 30px; align-items: flex-start; flex-wrap: wrap;\"\u003e\n\u003cdiv style=\"flex: 0 0 300px; order: 1;\"\u003e\u003cimg style=\"width: 100%; height: auto;\" alt=\"Deep Robotics Lite3 Pro applications\" src=\"https:\/\/www.image2url.com\/r2\/default\/images\/1776206299339-aaaa9dfc-aabc-4499-84d3-0d7fd9f54ead.jpg\"\u003e\u003c\/div\u003e\n\u003cdiv style=\"flex: 1; order: 2; min-width: 300px;\"\u003e\n\u003cp\u003eThe Lite3 Pro is engineered for professional robotics research groups and engineering teams that require a high-performance quadruped platform with the computational headroom for advanced AI workloads. University robotics labs deploy the Lite3 Pro for research in reinforcement learning, sim-to-real transfer, and adaptive locomotion across varied terrain types. The NVIDIA Jetson Orin Nano's GPU acceleration enables real-time neural network inference for perception, planning, and control tasks that would overwhelm CPU-only platforms.\u003c\/p\u003e\n\u003cp\u003eCommercial applications span autonomous inspection of industrial facilities, security patrol in complex indoor-outdoor environments, and development of custom robotic solutions requiring both mobility and onboard intelligence. The dual RealSense depth cameras provide stereo perception for sophisticated obstacle avoidance and environment mapping, while the ROS2 ecosystem compatibility enables rapid integration with existing robotics software stacks. The Lite3 Pro bridges the gap between educational platforms and full industrial-grade quadrupeds, offering professional-tier capability at a research-accessible price point.\u003c\/p\u003e\n\u003cp\u003eThe combination of dual depth cameras, GPU computing, and the Lite3 mechanical platform creates a self-contained development system that eliminates external computing dependencies during field testing. Research groups publishing in top-tier robotics venues use the Lite3 Pro to validate perception-locomotion integration approaches that would be prohibitively expensive to develop on larger industrial platforms.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003chr\u003e\n\u003ch3\u003eSetup and Getting Started\u003c\/h3\u003e\n\u003cdiv style=\"display: flex; gap: 30px; align-items: flex-start; flex-wrap: wrap;\"\u003e\n\u003cdiv style=\"flex: 1; order: 1; min-width: 300px;\"\u003e\n\u003cul\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cstrong\u003eEstimated Setup Time:\u003c\/strong\u003e\u003cspan\u003e 1 to 2 hours for unboxing, charging, component verification, GPU initialization, and running the first autonomous navigation demo.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cstrong\u003eStep 1 - Unbox and Verify Components:\u003c\/strong\u003e\u003cspan\u003e Confirm all items: Lite3 Pro unit with factory-mounted dual Intel RealSense D435i depth cameras, NVIDIA Jetson Xavier NX perception computer with pre-installed dev environment, rechargeable lithium battery (18 Ah high-capacity), AC charger, Ethernet cable, quick start guide, GPU computing reference, and SDK documentation.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cstrong\u003eStep 2 - Charge the Battery:\u003c\/strong\u003e\u003cspan\u003e Fully charge the 18 Ah battery (approximately 1.5 hours). This higher-capacity battery provides 2 to 2.5 hours of runtime with cameras and SLAM active, or 4 to 5 hours on standby.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cstrong\u003eStep 3 - Power On and Boot:\u003c\/strong\u003e\u003cspan\u003e\u003cstrong\u003e \u003c\/strong\u003ePlace the robot on a flat, open surface. Power on and allow approximately 2 minutes for the full boot sequence, including GPU initialization, camera calibration, and sensor validation across the split-brain architecture (RK3588 motion host + Jetson Xavier NX perception host).\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cstrong\u003eStep 4 - Network Connection:\u003c\/strong\u003e\u003cspan\u003e Connect to the Jetson via WiFi or Ethernet (USB 3.0 and HDMI also available). The Pro supports external power input at 24V, 12V, and 5V for extended development sessions.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cstrong\u003eStep 5 - Install SDK and ROS Packages:\u003c\/strong\u003e\u003cspan\u003e Install the Deep Robotics SDK and ROS2 packages on your development machine. The system runs Ubuntu 20.04 with ROS1 (Noetic) and ROS2 (Foxy) switching support, CUDA-enabled TensorFlow and PyTorch, and YOLOv8.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cstrong\u003eStep 6 - Run Diagnostics:\u003c\/strong\u003e\u003cspan\u003e Execute the built-in diagnostic suite to verify all 12 DOF, both depth cameras, GPU compute, and sensor fusion are operational.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cstrong\u003eStep 7 - First Autonomous Demo:\u003c\/strong\u003e\u003cspan\u003e Run the sample autonomous navigation demo to see the stereo depth perception and SLAM capabilities in action. The Pro handles 40-degree slopes, 15 cm stairs, and reaches 4 m\/s at full speed.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\u003cspan\u003e\u003cb id=\"docs-internal-guid-0f34b354-7fff-2560-fea3-426513600d62\"\u003eIP Rating: \u003c\/b\u003eIP54 splash-resistant, suitable for outdoor field testing.\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003cdiv style=\"flex: 0 0 300px; order: 2;\"\u003e\u003cimg style=\"width: 100%; height: auto;\" alt=\"Deep Robotics Lite3 Pro setup and getting started\" src=\"https:\/\/www.image2url.com\/r2\/default\/images\/1776206328191-428fec5d-b109-4317-bb65-d3ab48a77cbb.jpg\"\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003chr\u003e\n\u003ch3\u003eWhat's Included\u003c\/h3\u003e\n\u003cul style=\"list-style-type: none; padding-left: 0; margin-left: 0;\"\u003e\n\u003cli\u003e\n\u003cstrong\u003eRobot Unit:\u003c\/strong\u003e Deep Robotics Lite3 Pro quadruped with dual depth cameras and GPU computing\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDepth Cameras:\u003c\/strong\u003e Two Intel RealSense depth camera modules factory-mounted and calibrated\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eComputing Module:\u003c\/strong\u003e NVIDIA Jetson Orin Nano with pre-installed development environment\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBattery Packs:\u003c\/strong\u003e Rechargeable lithium batteries for extended operation\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCharger:\u003c\/strong\u003e AC power adapter for battery charging\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eEthernet Cable:\u003c\/strong\u003e Network cable for development workstation connection\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDocumentation:\u003c\/strong\u003e Quick start guide, GPU computing reference, and SDK documentation\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSoftware Access:\u003c\/strong\u003e Deep Robotics SDK, ROS2 packages, and AI model deployment tools\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCommunity Access:\u003c\/strong\u003e Developer forum and professional support registration\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003chr\u003e\n\u003ch3\u003eDocumentation and Resources\u003c\/h3\u003e\n\u003cul style=\"list-style-type: disc; padding-left: 0; margin-left: 0; list-style-position: inside;\"\u003e\n\u003cli\u003e\n\u003cstrong\u003eLite3 AI User Manual (PDF):\u003c\/strong\u003e \u003ca href=\"https:\/\/www.deeprobotics.us\/wp-content\/uploads\/2025\/08\/Jueying-Lite3-AI-User-Manual-V1.0.3-0.pdf\"\u003ehttps:\/\/www.deeprobotics.us\/wp-content\/uploads\/2025\/08\/Jueying-Lite3-AI-User-Manual-V1.0.3-0.pdf\u003c\/a\u003e\u003cstrong\u003e\u003c\/strong\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLite3 Perception Development Manual (PDF):\u003c\/strong\u003e \u003ca href=\"https:\/\/static.generation-robots.com\/media\/lite-3-perception-development-manual.pdf\"\u003ehttps:\/\/static.generation-robots.com\/media\/lite-3-perception-development-manual.pdf\u003c\/a\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cmeta charset=\"utf-8\"\u003e \u003cstrong\u003eDeep Robotics GitHub Organization:\u003c\/strong\u003e \u003ca href=\"https:\/\/github.com\/DeepRoboticsLab\/\"\u003ehttps:\/\/github.com\/DeepRoboticsLab\/\u003c\/a\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cmeta charset=\"utf-8\"\u003e \u003cstrong\u003eLite3 Motion SDK:\u003c\/strong\u003e \u003ca href=\"https:\/\/github.com\/DeepRoboticsLab\/Lite3_MotionSDK\"\u003ehttps:\/\/github.com\/DeepRoboticsLab\/Lite3_MotionSDK\u003c\/a\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cmeta charset=\"utf-8\"\u003e \u003cstrong\u003eLite3 ROS Package:\u003c\/strong\u003e \u003ca href=\"https:\/\/github.com\/DeepRoboticsLab\/Lite3_ROS\"\u003ehttps:\/\/github.com\/DeepRoboticsLab\/Lite3_ROS\u003c\/a\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003chr\u003e\n\u003ch3\u003eWarranty Information\u003c\/h3\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThis product is covered by a 12-Month Limited Warranty. The warranty covers defects in materials and workmanship under normal use.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch4\u003eCovered:\u003c\/h4\u003e\n\u003cp dir=\"ltr\"\u003eDeep Robotics Lite3 Pro: 12-Month (1-Year) Full-Unit Warranty\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eCore Components (battery, joints): 6-Month Warranty\u003c\/span\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\u003ch4 dir=\"ltr\"\u003e\u003cspan\u003eNot Covered:\u003c\/span\u003e\u003c\/h4\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e- Damage from misuse, negligence, or unauthorized modification\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e- Normal wear and tear on consumable components\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e- Damage from use outside recommended operating conditions\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e- Unauthorized repairs or disassembly\u003c\/span\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eAll warranty claims require valid proof of purchase.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e","brand":"Deep Robotics","offers":[{"title":"Default Title","offer_id":43884290670680,"sku":"L3-300-00-05","price":12510.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0659\/1437\/2184\/files\/deep-robotics-lite3-pro-quadruped-robot-industrial-inspection-research.webp?v=1773869765","url":"https:\/\/roboticsselect.com\/products\/deep-robotics-lite3-pro","provider":"RoboticsSelect","version":"1.0","type":"link"}