{"product_id":"deep-robotics-lite3-basic-copy","title":"Deep Robotics Lite3 Basic AI","description":"\u003c!-- PRODUCT: Deep Robotics Lite3 Basic AI --\u003e\n\u003ch2 style=\"text-align: center;\"\u003eDeep Robotics Lite3 Basic AI\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 Basic AI extends the proven Lite3 Basic platform with integrated artificial intelligence capabilities, adding an Intel RealSense depth camera and NVIDIA Jetson GPU computing module to the base quadruped hardware. This combination transforms the platform from a locomotion research tool into a complete autonomous robotics development system capable of real-time perception, obstacle avoidance, and AI-driven navigation. The AI-optimized locomotion gaits, trained through advanced reinforcement learning, deliver noticeably superior stability and terrain adaptability compared to conventional control approaches.\u003c\/p\u003e\n\u003cp\u003eThe reinforcement learning pipeline is a core differentiator. Using the official Lite3_rl_deploy GitHub repository, developers can train custom locomotion policies in simulation using PyTorch or TensorFlow, then deploy them directly to hardware through a streamlined sim-to-real transfer process. This capability makes the Lite3 Basic AI particularly valuable for graduate-level research in embodied AI, where the ability to iterate rapidly between simulated and physical experiments is essential for productive research outcomes.\u003c\/p\u003e\n\u003cp\u003eHardware specifications match the standard Lite3 platform: 12 kg total weight, 5 kg continuous payload, 4 m\/s top speed, 40-degree slope climbing, 15 cm stair capability, and 1.5 to 2 hours of battery life. The onboard GPU provides sufficient compute for running perception neural networks, SLAM algorithms, and control policies simultaneously, eliminating the need for external computing infrastructure during field experiments and demonstrations.\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 Basic AI quadruped robot with AI capabilities\" src=\"https:\/\/image2url.com\/r2\/default\/images\/1773873637960-d5d82ce0-b571-413b-aca1-4f30b9a17d15.png\"\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 Basic AI AI enhancement architecture\" src=\"https:\/\/image2url.com\/r2\/default\/images\/1773876380814-a80baa94-1d39-494f-990d-7e3d87591613.png\"\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 × 406 mm (Standing)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eWeight:\u003c\/strong\u003e 12 kg (including battery)\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 5 km maximum\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 processor\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eControl Frequency:\u003c\/strong\u003e Up to 1 kHz\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\u003eAI Gait Optimization:\u003c\/strong\u003e Reinforcement learning-trained locomotion policies\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eRL Framework Support:\u003c\/strong\u003e PyTorch and TensorFlow compatible\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eReinforcement Learning Deployment:\u003c\/strong\u003e Lite3_rl_deploy GitHub repository with examples\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOperating System Support:\u003c\/strong\u003e Ubuntu 20.04 compatible\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSoftware Frameworks:\u003c\/strong\u003e ROS1 (Noetic) and ROS2 (Foxy)\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\u003eCommunication:\u003c\/strong\u003e Wireless remote control with advanced feedback\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eUpgrade from Basic:\u003c\/strong\u003e Includes AI-optimized motion algorithms and RL training environment\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDepth Camera:\u003c\/strong\u003e Intel RealSense stereo depth camera for 3D environment perception\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAI Compute Module:\u003c\/strong\u003e NVIDIA Jetson-series GPU for onboard neural network inference\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDegrees of Freedom:\u003c\/strong\u003e 12 total (3 per leg) providing full quadruped locomotion capability\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eIMU Sensor:\u003c\/strong\u003e 9-axis inertial measurement unit for real-time orientation and acceleration data\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 polymer panels\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\u003eAI-Optimized Motion:\u003c\/strong\u003e Locomotion gaits trained through advanced reinforcement learning for superior stability\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eEnhanced Adaptability:\u003c\/strong\u003e AI algorithms dynamically adjust gait patterns for varying terrain conditions\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eReinforcement Learning Environment:\u003c\/strong\u003e Integrated support for training custom policies via Lite3_rl_deploy\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSimulation-to-Real (Sim-to-Real) Pipeline:\u003c\/strong\u003e Train policies in simulation and deploy directly to hardware\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eImproved Stability:\u003c\/strong\u003e RL-based optimization provides enhanced balance and responsiveness\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMulti-Framework Support:\u003c\/strong\u003e Compatible with PyTorch and TensorFlow for AI development\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e50% Increased Joint Torque:\u003c\/strong\u003e More powerful actuators enable aggressive maneuvers\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eExtended Battery Life:\u003c\/strong\u003e 1.5–2 hours continuous operation per charge\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e1 kHz Control Loop:\u003c\/strong\u003e Real-time responsiveness for dynamic movement execution\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSecondary Development Framework:\u003c\/strong\u003e Comprehensive SDK for implementing custom AI applications\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGitHub Repository Support:\u003c\/strong\u003e Official examples and tools for RL development pipeline\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eROS1 and ROS2 Compatibility:\u003c\/strong\u003e Seamless integration with both robotics operating systems\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAdvanced Motion Capabilities:\u003c\/strong\u003e Supports jumps, flips, and complex dynamic movements\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eResearch-Grade Platform:\u003c\/strong\u003e Purpose-built for academic AI and robotics research\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eRobust Terrain Handling:\u003c\/strong\u003e AI-enhanced algorithms master slopes, stairs, and irregular terrain\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOnboard Depth Perception:\u003c\/strong\u003e Intel RealSense camera provides real-time 3D environment mapping without external processing\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGPU-Accelerated Inference:\u003c\/strong\u003e NVIDIA Jetson module runs neural networks locally for autonomous decision-making\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAutomatic Self-Recovery:\u003c\/strong\u003e AI-enhanced righting algorithms return the robot to standing after falls\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\u003eVisual Obstacle Avoidance:\u003c\/strong\u003e Depth camera data feeds real-time obstacle detection and path replanning\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLightweight Portability:\u003c\/strong\u003e 12 kg total weight enables single-person transport and rapid field deployment\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 Basic AI demonstrating advanced movement control\" src=\"https:\/\/www.image2url.com\/r2\/default\/images\/1776124368061-80ac9c46-fc08-443f-9efe-7755dafb24c6.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 Basic AI applications\" src=\"https:\/\/www.image2url.com\/r2\/default\/images\/1776124392097-d1de706a-8425-4a10-bd76-3503720b76b6.jpg\"\u003e\u003c\/div\u003e\n\u003cdiv style=\"flex: 1; order: 2; min-width: 300px;\"\u003e\n\u003cp\u003eThe Lite3 Basic AI targets advanced research groups and development teams working at the intersection of computer vision, autonomous navigation, and legged robotics. Its integrated Intel RealSense depth camera and NVIDIA Jetson computing module provide the hardware foundation for developing and testing perception-driven locomotion algorithms, SLAM implementations, and obstacle avoidance strategies in real-world environments. Universities leverage this platform for graduate-level research in embodied AI, while corporate labs use it for prototyping autonomous inspection and monitoring solutions.\u003c\/p\u003e\n\u003cp\u003eField applications include indoor facility inspection where the robot autonomously maps environments and identifies anomalies, outdoor terrain survey missions requiring visual obstacle classification, and security patrol scenarios that demand real-time environmental awareness. The platform also serves as a development testbed for startups building commercial quadruped applications, enabling rapid iteration on perception algorithms before deploying to larger, more expensive industrial platforms.\u003c\/p\u003e\n\u003cp\u003eThe integrated sim-to-real pipeline accelerates research publication timelines by enabling rapid iteration between simulation experiments and physical validation. Teams working on reinforcement learning for locomotion, visual navigation, and multi-modal perception find the Lite3 Basic AI provides sufficient compute and sensor capability to produce publishable results without the cost and complexity of custom-built research hardware.\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 30 to 45 minutes for unboxing, battery charging, and first walk test.\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 Inspect:\u003c\/strong\u003e\u003cspan\u003e Remove the Lite3 Basic AI from packaging. Verify all components: robot unit, 28.8V\/4.4Ah lithium-ion battery, AC charger, wireless gamepad controller, and 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 battery using the included charger (approximately 40 to 60 minutes). The hot-swappable battery provides 1.5 to 2 hours of runtime.\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 - Insert Battery and Power On:\u003c\/strong\u003e\u003cspan\u003e Install the charged battery and place the robot on a flat, open surface with ample clearance. Power on and wait approximately 60 seconds for the system to boot.\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 - Controller Setup:\u003c\/strong\u003e\u003cspan\u003e Power on the wireless gamepad controller; it pairs automatically. The Basic AI variant uses reinforcement-learning-trained gaits for enhanced stability and adaptability across varied terrain.\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 the Companion App:\u003c\/strong\u003e\u003cspan\u003e Download the DEEP Robotics app (Android 6.0 or later). Connect to the robot's WiFi network for additional control options and telemetry monitoring.\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 - Test Locomotion:\u003c\/strong\u003e\u003cspan\u003e Walk the robot across different surfaces to experience the RL-enhanced gaits. The Basic AI model does not include perception sensors or an onboard AI computer; external cameras or LiDAR can be added via the mounting points if desired.\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-ea9b1b20-7fff-127a-4aba-cb0fa1842598\"\u003eDeveloper Access: \u003c\/b\u003eC++ SDK, Python SDK, and ROS1\/ROS2 compatibility are available for custom application development.\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 Basic AI setup and getting started\" src=\"https:\/\/www.image2url.com\/r2\/default\/images\/1776124417575-c20d52fb-d733-449f-8876-3b3d5bd6a1d1.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 Basic AI quadruped with pre-integrated AI computing module\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDepth Camera:\u003c\/strong\u003e Intel RealSense depth camera module factory-mounted and calibrated\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eComputing Module:\u003c\/strong\u003e NVIDIA Jetson computing unit pre-installed with development environment\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBattery Pack:\u003c\/strong\u003e Rechargeable lithium battery with charge management circuitry\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\u003eDocumentation:\u003c\/strong\u003e Quick start guide, AI module reference, and SDK documentation\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSoftware Access:\u003c\/strong\u003e Deep Robotics SDK, ROS2 packages, and sample AI applications\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCommunity Access:\u003c\/strong\u003e Developer forum and research community 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\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\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\u003ch4 dir=\"ltr\"\u003e\u003cspan\u003eCovered:\u003c\/span\u003e\u003c\/h4\u003e\n\u003cp dir=\"ltr\"\u003eDeep Robotics Lite3 Basic AI: 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":43884277465176,"sku":"L3-110-01-02","price":4968.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0659\/1437\/2184\/files\/deep-robotics-lite3-basic-quadruped-robot-industrial-inspection-research.webp?v=1773796049","url":"https:\/\/roboticsselect.com\/products\/deep-robotics-lite3-basic-copy","provider":"RoboticsSelect","version":"1.0","type":"link"}