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Helpful DoggyBot
Overview :
Helpful DoggyBot is an indoor mobility operating system for quadrupedal robots that manipulates objects using a front-end gripper and leverages low-level controllers trained in simulated environments to achieve agility skills like climbing and full-body inclination. Additionally, it incorporates pre-trained visual-language models (VLMs) for semantic understanding and command generation. The system can generalize to unseen environments to accomplish tasks with zero-shot learning, such as retrieving a randomly placed toy from beside a bed after climbing, with a success rate of 60%, without any actual data collection or training.
Target Users :
Helpful DoggyBot is designed for researchers, robotics enthusiasts, and professionals in the automation field. It provides a platform for researching and developing quadrupedal robots to explore their applications in home environments, rescue situations, and other contexts that require complex interactions.
Use Cases
Retrieving toys as per user commands in a home environment
Climbing obstacles and performing search tasks in rescue scenarios
Handling goods in an automated warehouse
Features
Front-end gripper for object manipulation
Low-level controllers trained in simulated environments enabling agile skills like climbing and full-body inclination
Utilizes pre-trained visual-language models (VLMs) for semantic understanding and command generation
No actual data collection or training needed for zero-shot task completion in new environments
Can understand user commands and execute corresponding actions
High reachability and traversability in indoor environments
Supports interaction with humans while performing indoor skill tasks
How to Use
1. Install and configure the required hardware and software environment
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2. Download and install the Helpful DoggyBot code
3. Set up the robot's low-level controllers according to the documentation
4. Configure the visual-language model to understand specific commands
5. Conduct initial tests in a simulated environment to ensure the robot can perform basic tasks
6. Deploy the robot in real-world settings and perform task testing
7. Adjust the robot's parameters and operation logic based on testing results
8. Continuously monitor the robot's performance and perform necessary maintenance
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