Robotics AI Task Automation AI project
Budget / SalaryHourly project
TypeFreelance project
LocationRemote
Posted2 hours ago
The goal is to deliver a complete AI-driven robotics solution that automates a clearly defined task while simultaneously boosting decision-making quality and overall efficiency. My emphasis is on three tightly linked capabilities: reliable navigation and mapping (SLAM on a mobile base), precise object manipulation (pick-and-place with force feedback), and natural human-robot interaction (simple voice or touch-screen prompts that adapt to context).
I am lookng for AI specialist partners who can assist me with european based projects who are maybe in asia or americas.
Core expectations
• A ROS-compatible navigation stack that builds and updates maps in real time and feeds an expert-system layer for path selection.
• A manipulation pipeline using MoveIt! or a comparable planner that can recognise, grasp, and relocate objects of varying size and weight.
• An interaction module that calls the expert system to decide when to approach, ask, or act, then logs each decision for later analysis.
• Clean, well-commented code (Python/C++), launch files, simulation assets, and a short demo video that proves the three modules working together on either Gazebo or a physical testbed.
• Setup and usage documentation clear enough for a junior engineer to replicate.
When you reply, focus on your direct experience building similar navigation, manipulation, and HRI stacks—links to past repositories, videos, or publications help me gauge fit quickly.
I am lookng for AI specialist partners who can assist me with european based projects who are maybe in asia or americas.
Core expectations
• A ROS-compatible navigation stack that builds and updates maps in real time and feeds an expert-system layer for path selection.
• A manipulation pipeline using MoveIt! or a comparable planner that can recognise, grasp, and relocate objects of varying size and weight.
• An interaction module that calls the expert system to decide when to approach, ask, or act, then logs each decision for later analysis.
• Clean, well-commented code (Python/C++), launch files, simulation assets, and a short demo video that proves the three modules working together on either Gazebo or a physical testbed.
• Setup and usage documentation clear enough for a junior engineer to replicate.
When you reply, focus on your direct experience building similar navigation, manipulation, and HRI stacks—links to past repositories, videos, or publications help me gauge fit quickly.
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