Context :

At Stanley Robotics, we build a robotized parking solution that automatically stores cars, using a fleet of robots. In order to maximize the efficiency of our product, we need robots that can navigate smoothly in the yard, including in narrow areas, while surrounded by vehicles. 

Your mission :

Join the Robotsoft team at Stanley Robotics, where you will work alongside our R&D team of experienced developers from various backgrounds, in a challenging, creative and friendly atmosphere. You will select one of the two key tracks to advance the field of robust robot navigation, with the shared objective of ensuring obstacle avoidance in narrow lanes while accounting for the robot’s operational and physical constraints.

1. Track 1: Model-Free Navigation with Reinforcement Learning (RL):
   You will implement and test new motion planning and control algorithms using RL techniques tailored for real-world applications.

2. Track 2: Model Predictive Control (MPC) for Motion Planning and Control:
   You will design and implement MPC-based navigation algorithms that account for the system’s limitations while operating within tight runtime constraints.

Responsibilities:

• Design, implement, and test motion planning and control algorithms in C++/Python

• Focus on obstacle avoidance in constrained environments, such as narrow lanes, while respecting the robot’s limitations (e.g., speed, acceleration, steering angle, etc.)

• Explore and implement state-of-the-art methods for robot navigation, including MPC- or RL-based techniques

• Document and present findings, including testing results and performance analysis

Your profile :

• Final-year engineering student (6-month internship minimum)

• Good programming skills in C++ and Python

• Experience in ROS, Linux, Git ecosystems

• Knowledge of reinforcement learning and control theory 

• Knowledge of machine learning and embedded optimization tools is a plus

• Capable of working independently on complex challenges

• French and English at a professional level