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An open source quadruped robot pet framework for developing Boston Dynamics-style four-legged robots that are perfect for STEM, coding & robotics education, IoT robotics applications, AI-enhanced robotics application services, research, and DIY robotics kit development.
An ESP32-based open source quadruped robot pet framework for developing Boston Dynamics-style four-legged robots that are perfect for STEM, coding & robotics education, IoT robotics applications, AI-enhanced robotics application services, research, and DIY robotics kit development.
Developing a four-legged Quadruped robot 'DIPLOID' with stable walking by reinforced learning on bezier gait and terrain awareness using SLAM technique as a part of long-term project undertaken by Team Robocon. (2019-present)
This project implements a Proximal Policy Optimization (PPO) reinforcement learning agent to train the Minitaur robot to walk in the MinitaurBulletEnv-v0 environment using PyBullet. The agent uses a multilayer perceptron (MLP) to model the policy and value networks and learns to control the robot in a continuous action space.