Pupper
The Pupper team builds AI policies to create an intelligent quadruped robot that can interact with its environment.
Background
The Pupper team works with Hands-on Robotics’ Stanford Pupper robot to advance human-robot interaction, envisioning a quadruped robot adept at interpreting environmental cues and responding to gestural commands from human, leveraging its innate stability and agility across diverse terrains through biomimetic locomotion.
Goals
The team has three key tasks: controlling robotic movement using Raspberry Pi, training new reinforcement learning policies in Python, and live image tracking with computer vision. The team aims to achieve seamless robotic operation without manual controllers, enabled by cloud-based computing for advanced reinforcement learning policies. Additionally, we're integrating Oak-D AI cameras for real-time object tracking and training robust reinforcement learning policies to ensure our robot can navigate autonomously and efficiently.
Relevant Skills
Python, Reinforcement Learning (RL), Computer Vision (CV), Raspberry Pi.