Robotics AI Task Automation AI project

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@Mahihassi

With my advanced degree in Embedded Systems and specialization in firmware development, I offer a unique edge for this project. My experience with state-of-the-art microcontrollers like STM32, ESP32, and Raspberry Pi makes me a pro at wielding the technologies that power robotics in your project. Delving into your specific requirements, I feel confident about incorporating ROS-compatible navigation stacks and manipulation pipelines using MoveIt! to ensure a fully-integrated solution that tackles all the intricacies of tasks automation. What further sets me apart is my expertise in AI and Machine Learning which is crucial to make your system intelligent, enabling it to boost decision-making quality and overall efficiency. I have created intelligent algorithms for edge devices, control, and data analysis which resonates with your need for natural human-robot interaction (HRI). To exemplify my words with the presence of tangible results, I'd point to projects where I've successfully implemented AI-driven robotics systems. My commitment doesn't end with the code-delivery but extends into making sure that even a junior engineer can understand and recreate the solution by providingclear documentation.

@neerajdhameliya

Hello, I’d suggest keeping the system focused on the actual task the robot needs to complete, rather than treating navigation, manipulation, and HRI as separate pieces. The robot could understand the task, navigate to the right location, identify and pick the object, move it safely, and ask for human input only when something requires a decision. I can build the ROS/ROS2, SLAM, MoveIt!, force-feedback, and HRI components around this workflow, with Gazebo simulation, Python development, decision logging, documentation, and an end-to-end demo. Happy to discuss your robot platform, sensors, and target task. Best, Niral

@juanpablomacf

You asked for repositories, videos or publications first - so I'll be direct rather than pad a proposal. I have not built SLAM navigation stacks, MoveIt! manipulation pipelines or ROS-based HRI modules. The three capabilities you list are each a specialty: SLAM tuning on a mobile base, force-feedback grasping across varying weights, and a decision layer that logs every action. If I bid as if I'd shipped that stack, you'd find out in the first technical call and lose a week. Where I am genuinely useful: I work on AI workflow automation, API integration and the orchestration layer - the expert system that consumes map and perception state, decides approach/ask/act, logs each decision, and the tooling around it. That's the piece your spec calls the interaction module and expert-system layer, and it's the part usually left thin while the robotics core gets the attention. On risk: the demo video requirement is the real filter. Three modules "working together" in Gazebo is achievable; on a physical testbed with force feedback, the integration time typically dwarfs the coding time, and your hourly range suggests you may be underestimating that. Questions: is the expert-system/decision-logging layer already scoped, or open? Gazebo only, or is there a physical base? Do you already have ROS engineers on the navigation and manipulation sides? If the answer is yes to the last one, I'd rather take the decision layer than pretend to own the whole stack. Juan Pablo

@HamparProd

I would build this as a ROS 2 modular system where AI handles high-level decisions while navigation, manipulation and safety remain deterministic. Recommended stack: ROS 2 Jazzy + Nav2/SLAM Toolbox + MoveIt 2 + Gazebo Harmonic, with Python/C++ nodes. Architecture: SLAM/navigation → object perception → grasp planning → force-validated manipulation → expert task logic → voice/touch HRI → decision logging. My background is strongest in AI, computer vision, automation and real-time decision pipelines, so I would begin with a measurable simulation milestone before hardware-specific tuning. Initial milestone: • SLAM + autonomous navigation • MoveIt pick-and-place • simulated object recognition • basic voice/touch command flow • expert-system/task-state integration • decision/event logging • launch files and reproducible Gazebo environment • setup documentation and demo recording Physical force-sensor and robot-specific integration would follow once the target hardware is confirmed.

@ZohaibRoy

⭐⭐⭐⭐⭐ Build AI-Driven Robotics Solutions for Enhanced Efficiency ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and noticed you're looking for an AI specialist for robotics solutions. Look no further; Zohaib is here to help you! My team has successfully completed 50+ similar projects in AI-driven robotics. I will create a robust solution focusing on navigation, manipulation, and human-robot interaction using efficient methods and within your budget. ➡️ Why Me? I can easily do your robotics project as I have 5 years of experience in AI, robotics, and automation. My expertise includes ROS development, SLAM, object manipulation, and human-robot interaction. Not only this, but I also have a strong grip on Python and C++, ensuring a complete approach to your project. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. I look forward to discussing this with you in our chat. ➡️ Skills & Experience: ✅ ROS Development ✅ SLAM Implementation ✅ Object Manipulation ✅ MoveIt! Planning ✅ Human-Robot Interaction ✅ Python Programming ✅ C++ Programming ✅ Real-time Mapping ✅ Expert System Integration ✅ Decision Logging ✅ Simulation in Gazebo ✅ Documentation Writing Waiting for your response! Best Regards, Zohaib

@mughiraaa

hi, i can help build the robotics stack around ROS with navigation, manipulation and human robot interaction working as one system. i have experience with robotics software, Python, C++, ROS, SLAM, sensor integration, motion planning and AI based decision workflows. i can work on real time mapping, MoveIt based pick and place, force feedback and voice or touchscreen commands, with the expert system logging each decision. i’ll keep the code modular with simulation assets, launch files, documentation and a working Gazebo demo before moving toward physical testing. are you already using a specific ROS version and mobile robot platform? let’s have a quick meeting to discuss the first European project and the hardware or simulation setup. Mughiraa

@mcollinscalz

Hello, Delivering an end-to-end robotics pipeline requires seamless integration between autonomous navigation, adaptive manipulation, and context-aware decision logic. As an AI & Robotics Specialist based outside Europe, I provide high-efficiency, international partnership capabilities for European-based automation projects. How I Will Build Your Architecture: Navigation & SLAM: Deploy ROS 2 / Nav2 with real-time RTAB-Map or Cartographer, integrated with an expert-system layer for dynamic local path selection and obstacle avoidance. Manipulation & Grasping: Implement a MoveIt 2 manipulation pipeline featuring OpenCV/Point Cloud perception for multi-object recognition, trajectory planning, and simulated/hardware force-feedback compliance. HRI & Decision Engine: Build a Python-based context engine using state machines or LLM logic to parse voice/touch prompts, trigger active robot behaviors, and maintain decision logs. Deliverables: Clean C++/Python source packages, launch files, and URDF/Gazebo simulation assets. Video demonstration showing the full autonomous SLAM -> Grasp -> HRI workflow. Replicable setup guide for your engineering team. I have extensive experience deploying ROS-driven mobile manipulators in Gazebo and on physical hardware. I am ready to share past project repositories and video demos upon request. Best regards,

@jeffleads

Hello. I have hands-on experience building robotics and AI automation systems involving ROS, autonomous navigation, SLAM, computer vision, manipulation workflows, and intelligent decision layers, so this is exactly the kind of integration where I can turn separate modules into one working system. 1. Which mobile base, LiDAR/depth camera, robotic arm, and force sensor hardware are you planning to use, or should I make the stack hardware-agnostic initially? 2. Do you already have an expert-system architecture and preferred ROS/Gazebo version, or would you like me to design the decision and logging layer as part of the implementation? Your answers will greatly help me understand the target environment; I would approach this by first validating SLAM/navigation, then integrating perception and force-aware MoveIt manipulation, and finally connecting HRI to the expert decision layer with complete logging and simulation validation. I can start immediately and will focus on delivering a demonstrable, maintainable ROS solution rather than isolated prototypes, with clean Python/C++, reproducible setup, documentation, and a demo proving the three capabilities work together. Let's discuss this project further Best regards, Jeff Chong.

@huzaifa306

Hi, As a machine learning engineer, I can help build the complete robotics pipeline across SLAM navigation, object manipulation, and human robot interaction. I will connect the ROS navigation stack with MoveIt, the expert decision layer, and voice or touch interaction, then test the full workflow in simulation or on the available hardware. I will provide clean code, launch files, simulation assets, documentation, and a working demo. Best regards, Huzaifa

@hashonecreatives

Hi there, I noticed you’ve posted multiple projects, and I’d definitely be interested in exploring how we can support you across them. Rather than going back and forth trying to cover everything in the bidding chat, I’d prefer a quick Freelancer call where we can understand your priorities, share our relevant experience, and discuss the best way to move forward. If the projects are still open, let’s connect for a quick discussion. I’m confident we can identify where our team can add the most value and get things moving without overcomplicating the initial conversation. Looking forward to connecting with you!

@Qupati

My most relevant work is a JAKA Zu5 manipulation project combining deep learning-based 6D pose estimation with adaptive compliance control for nut grasping and tightening. The approach uses image segmentation, a CAD-trained Augmented Autoencoder for orientation estimation, and depth information for translation. Handling symmetric hex nuts is a particular challenge, making this work relevant to your object recognition and manipulation requirements. My related experience includes: • **Navigation:** ROS2 mobile robot projects involving TurtleBot3, LiDAR, odometry, IMU integration, and simulation, including multi-robot hospital scenarios. • **Manipulation:** ROS2, MoveIt2, Gazebo, and industrial robot integration, connecting perception outputs to grasp planning and compliant motion. • **Human–robot interaction:** Work on local voice AI pipelines combining speech recognition, language models, and speech synthesis, with attention to response latency and hardware constraints. I would start with one measurable end-to-end task—for example, navigating to a workstation, identifying and relocating an object, and reporting completion—then validate navigation accuracy, grasp success, interaction latency, and recovery behavior. Relevant manipulation demo: In my profile Which mobile base, manipulator, force sensor, and ROS distribution are you currently using? Are you seeking a partner for an existing platform or development from the initial prototype? Best regards, Hussien Ali

@murtaza1122

I specialize in computer vision, edge ai. I can build the vision-guided manipulation, and expert-system decision layers as one integrated and reproducible solution. Have you already selected the mobile base, robotic arm, lidar/depth camera, and force torque sensor, or should we recommend the hardware stack.

@jaroslavcaprata

Hi, I’m interested in supporting your robotics AI project, particularly the integration between autonomous navigation, manipulation, human interaction, and the expert decision layer. I can contribute across: • Python/C++ AI and automation logic • ROS-compatible service/module integration • Decision and rule-based orchestration • Voice/NLP interaction workflows • Context-aware task handling • Logging and traceability of robot decisions • Simulation-side integration and testing • Clean APIs between navigation, manipulation, and HRI modules • Documentation and reproducible setup I understand the target architecture needs to coordinate SLAM/navigation, MoveIt-style manipulation, and HRI as one reliable system rather than treating them as isolated demos. I’d structure the solution around clear module boundaries so navigation, perception/manipulation, interaction, and decision logic can be tested independently and then validated end-to-end in Gazebo or on hardware. I also want to be transparent that I would only claim direct ROS/SLAM/MoveIt project experience where I can provide supporting examples. Best regards, Jaroslav

@Aaleen110

The constraint is a ROS-compatible navigation stack feeding an expert-system layer for path selection. I will build the SLAM component using ROS 2 Navigation2, which handles real-time mapping and navigation, also I will integrate a custom expert system for path selection logic, this will take the map and current goal and decide the best route. For object manipulation, I'll use MoveIt! with its Force Feedback integration for precise pick-and-place actions, also I will develop a simple voice and touch interface that adapts to task context. I would build the navigation stack first, because that forms the basis for all other actions the robot needs to take. What specific hardware platform will the robot be running on? Preferred Freelancer here, 5.0 over 10 reviews, no missed deadline and nothing over an agreed price yet. I will have a functional ROS 2 navigation stack with real-time mapping and a basic path selection algorithm working within the first day or two.

@dalexey025

Hi, I’d be interested in supporting your AI robotics projects and working with your team on European-based projects. My background is in Python, AI/LLM integrations, APIs, and full-stack development, and I’m comfortable working on complex systems where AI, automation, and software components need to work together. I can contribute to areas such as: Python-based AI and automation systems ROS/ROS2 integrations and robotics APIs AI decision-making and expert-system logic Voice/NLP interaction Backend services and data logging Simulation and integration testing Clean, documented code and technical documentation For the robotics stack, I can work with ROS2, MoveIt, Gazebo, Python/C++, and related tools, depending on the requirements of each project. I’m also comfortable working remotely with international teams and can support projects on an ongoing basis. If you share the first project requirements, I’d be happy to discuss the architecture, my role, timeline, and estimated cost. Best regards, Alexey

@sufyanasmat38

Yes, I’ve worked on Vision AI and real-time IoT/sensor-based projects where I handled live data processing and real-time results. This project is similar in terms of processing hardware data in real time.

@DrSyafiq

I can take this from scope to a tested handoff without bloating the first milestone. You're trying to add AI into a Robotics AI Task Automation AI project where the real challenge is making the AI layer reliable and the prompts survive real-world use. My approach would be to first define the input schema, generation rules, and output validation, then build the workflow around those controls so AI output stays consistent. Quick question: Which external integrations are absolutely required for MVP, and which can be staged after launch? Also, what real examples will define a correct AI output, and which failure cases must be blocked before launch? Best, Dr. Syafiq

@bojans9

Hi, Bojan here, from Serbia. Building your robotics AI task automation AI projects with three tightly linked capabilities is doable; what matters most is a device that behaves the same in the field as on the bench. I'm a senior embedded and electronics engineer with 8+ years of experience working with Robotics, Machine Learning (ML), AI Chatbot Development and AI Automation. With ROS, the real challenge is timing, power and real conditions. A common issue: testing only on the bench, so problems appear once the device runs in real conditions. My plan: first, review the schematic, pin map and timing requirements. Then, write the firmware in small modules, each tested on the board. Finally, test on the real hardware under load, power loss and bad signal. I've done firmware work before, so I check early for timing issues and power drops. A few questions to better understand the scope: 1. Should firmware update over the air? That needs a safe bootloader planned from the start. 2. Add a watchdog that restarts on a hang? I'd recommend it, no manual resets in the field. 3. Who uses the device, engineers or everyday users? Everyday users need clear status lights and defaults. I can start right away. Happy to go over the details here in chat and suggest the best approach. Looking forward to hearing from you, Bojan