{"product_id":"kit-pi-5-reward-shaping-robot-research","title":"Pi 5 Reward Shaping Robot Research","description":"\u003ch1\u003eAccelerate Reinforcement Learning Research with the Raspberry Pi 5 Reward Shaping Robot Kit\u003c\/h1\u003e\n \n\u003cp class=\"value-summary\"\u003eEvery part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.\u003c\/p\u003e\n \n\u003cdiv class=\"specs-strip\"\u003e\n  \u003cspan\u003e\u003cstrong\u003eDifficulty:\u003c\/strong\u003e Advanced\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 10-12 hrs\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 18-25\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e Potential-Based Reward Shaping\u003c\/span\u003e\n\u003c\/div\u003e\n \n\u003cp\u003eBuild a research-grade differential-drive robot and use it to implement potential-based reward shaping on the Raspberry Pi 5. You’ll train navigation policies with and without shaping, then compare convergence rates and final behaviour — just as a graduate RL lab would. This kit lets you move from theoretical planning to real‑world RL evaluation in a single project, using a platform that replicates genuine research workflows.\u003c\/p\u003e\n \n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eYou’ll assemble a fully mobile robot with the Pi 5 as the onboard brain, an NVMe SSD for low‑latency data logging, and a Cytron motor driver controlling two DC motors. The robot runs custom Gymnasium environments where you inject a potential function based on distance‑to‑goal. You’ll log Q‑values, loss curves, and success rates to visualize exactly where reward shaping cuts training time without altering the optimal policy.\u003c\/p\u003e\n \n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eImplement potential‑based reward shaping using handcrafted features and verify that it preserves the optimal policy\u003c\/li\u003e\n  \u003cli\u003eCompare sample efficiency of Q‑learning\/DQN variants with and without shaping on a real mobile robot\u003c\/li\u003e\n  \u003cli\u003eSet up a Raspberry Pi 5 with an NVMe SSD and M.2 HAT for high‑speed RL data handling and model checkpointing\u003c\/li\u003e\n  \u003cli\u003eInterface DC motors with a Cytron driver and integrate them into a closed‑loop RL loop using real‑time odometry feedback\u003c\/li\u003e\n\u003c\/ul\u003e\n \n\u003ch2\u003eKit Contents\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eComponent\u003c\/th\u003e\n\u003cth\u003eQuantity\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eRaspberry Pi 5 8GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eNVMe SSD 512GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePi 5 M.2 HAT+\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCytron Motor Driver\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eDC Motor\u003c\/td\u003e\n\u003ctd\u003e2\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eRobot Chassis\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eUSB-C PSU\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eM-M Wires\u003c\/td\u003e\n\u003ctd\u003e20\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n \n\u003ch2\u003eWhy Buy This Kit Instead of Sourcing Parts Separately\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eFactor\u003c\/th\u003e\n\u003cth\u003eSourcing Separately\u003c\/th\u003e\n\u003cth\u003eCompoden Kit\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCompatibility checks\u003c\/td\u003e\n\u003ctd\u003eYou verify every part\u003c\/td\u003e\n\u003ctd\u003ePre-tested as a system\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBuild support\u003c\/td\u003e\n\u003ctd\u003eForums and scattered tutorials\u003c\/td\u003e\n\u003ctd\u003eAI companion trained on this exact project\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eTime to first working build\u003c\/td\u003e\n\u003ctd\u003eDays of debugging\u003c\/td\u003e\n\u003ctd\u003eHours, with step-by-step guidance\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eShipping coordination\u003c\/td\u003e\n\u003ctd\u003eMultiple sellers, multiple delays\u003c\/td\u003e\n\u003ctd\u003eOne shipment from Bengaluru in 3-5 days\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n \n\u003ch2\u003eWho This Kit Is For\u003c\/h2\u003e\n\u003cp\u003eIdeal for B.Tech and M.Tech students at IITs, NITs, VIT, and BITS Pilani working on RL capstone projects, Smart India Hackathon teams building autonomous robots with fast learning cycles, and ATL Tinkering Lab mentors guiding advanced independent study. If you’re ready to move beyond simulation and compare shaped vs unshaped convergence on real hardware, this kit was built for you.\u003c\/p\u003e\n \n\u003ch2\u003eBuilt and Backed by Compoden\u003c\/h2\u003e\n\u003cp\u003eEvery Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.\u003c\/p\u003e\n \n\u003cdetails\u003e\u003csummary\u003eWhat if I get stuck during the build?\u003c\/summary\u003e\u003cp\u003eOpen the AI companion from the QR code on the box; it walks through every connection and can diagnose common miswiring. You can also message us on WhatsApp for real‑time help.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I run Python and RL libraries directly on the Pi 5?\u003c\/summary\u003e\u003cp\u003eYes, the NVMe SSD gives you fast I\/O for large replay buffers. You can install Gymnasium, Stable‑Baselines3, or TensorFlow Lite directly on the Pi 5 without external compute.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eHow exactly do I compare shaped and unshaped rewards?\u003c\/summary\u003e\u003cp\u003eThe AI companion guides you through coding a potential function based on distance to the goal. You’ll run both variants and export metrics like steps‑to‑goal and Q‑value convergence for a side‑by‑side comparison.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDoes this kit support sim‑to‑real transfer experiments?\u003c\/summary\u003e\u003cp\u003eAbsolutely. Train your policy in a simulated environment first, then load it onto the robot. The shaping function can help bridge the reality gap by shaping exploration behaviour in the same way.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003ePotential-based reward shaping on Pi 5 accelerates RL convergence without changing optimal policy — compare shaped vs unshaped.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-model-b-8gb-high-performance-single-board-computer\"\u003eRaspberry Pi 5 8GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/official-raspberry-pi-m2-hat-nvme-ssd-add-on-board-for-pi-5\"\u003eNVMe SSD 512GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-pcie-to-m2-nvme-ssd-expansion-board-by-elecrow\"\u003ePi 5 M.2 HAT+\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003eCytron Motor Driver\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/l293d-motor-driver-shield-for-arduino-drive-dc-stepper-motors\"\u003eDC Motor\u003c\/a\u003e x2\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/2-wheel-smart-car-robot-chassis-kit-diy-for-arduino-raspberry-pi\"\u003eRobot Chassis\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-4-official-power-supply-5v-3a-usb-c-compoden\"\u003eUSB-C PSU\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003eM-M Wires x20\u003c\/li\u003e\n  \u003c\/ul\u003e\n\u003c\/div\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is included in the Pi 5 Reward Shaping Robot Research?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Pi 5 Reward Shaping Robot Research includes all components needed: Raspberry Pi 5 8GB, NVMe SSD 512GB, Pi 5 M.2 HAT+, Cytron Motor Driver, DC Motor and more. Everything is pre-tested for compatibility and shipped from Bengaluru, India.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What skill level is required for the Pi 5 Reward Shaping Robot Research?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Advanced level makers, suitable for ages 18-25. Potential-based reward shaping on Pi 5 accelerates RL convergence without changing optimal policy — compare shaped vs unshaped. Estimated build time is 10-12 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Pi 5 Reward Shaping Robot Research online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Pi 5 Reward Shaping Robot Research is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. Ships from Bengaluru in 1-5 business days across India.\"\n      }\n    }\n  ]\n}\n\u003c\/script\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"Product\",\n  \"name\": \"Pi 5 Reward Shaping Robot Research\",\n  \"description\": \"Potential-based reward shaping on Pi 5 accelerates RL convergence without changing optimal policy — compare shaped vs unshaped.\",\n  \"sku\": \"CDN-KIT-2496\",\n  \"brand\": {\"@type\": \"Brand\", \"name\": \"Compoden\"},\n  \"offers\": {\n    \"@type\": \"Offer\",\n    \"url\": \"https:\/\/compoden.in\/products\/kit-pi-5-reward-shaping-robot-research\",\n    \"priceCurrency\": \"INR\",\n    \"price\": \"53400\",\n    \"availability\": \"https:\/\/schema.org\/InStock\",\n    \"seller\": {\"@type\": \"Organization\", \"name\": \"Compoden\"}\n  },\n  \"category\": \"AI Robotics\"\n}\n\u003c\/script\u003e","brand":"Compoden","offers":[{"title":"Default Title","offer_id":53469365076333,"sku":"CDN-KIT-2496","price":61580.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0999\/3997\/5533\/files\/kit-pi-5-reward-shaping-robot-research.png?v=1781948315","url":"https:\/\/compoden.com\/products\/kit-pi-5-reward-shaping-robot-research","provider":"Compoden","version":"1.0","type":"link"}