Intelligence & Perception

Dropbear is designed to be an embodied AI platform, capable of perceiving its environment and making intelligent decisions. Our approach combines classic robotics techniques with modern machine learning.

Perception Stack

The robot's understanding of the world is built on a suite of sensors processed by our perception stack. This includes:

• 3D Vision: Using depth cameras, the robot constructs a real-time 3D model of its surroundings for obstacle avoidance and object recognition.
• Object Detection: We leverage pre-trained models (like YOLO) to identify and locate common objects in the environment.
• State Estimation: Fusing data from IMUs and joint encoders, the robot maintains an accurate estimate of its position and orientation.

Large Language Model (LLM) Integration

To enable natural language interaction, we are integrating Large Language Models (LLMs). This allows users to give Dropbear high-level commands in plain English, such as "pick up the red block and place it on the table."

The LLM acts as a "task planner," breaking down the command into a sequence of actionable steps that the Corgi controller can execute. This bridges the gap between human language and low-level robot control.

Future Directions

Our roadmap is focused on increasing the robot's autonomy and intelligence. Key areas of research include:

• Reinforcement Learning: Training policies for complex motor skills like navigating uneven terrain or opening doors.
• Vision-Language Models (VLMs): Enabling a deeper understanding of the visual world through natural language.
• Generative Models: Using generative AI to create novel behaviors and solutions to problems on the fly.

As an open-source project, we invite researchers and developers to contribute their own models and ideas to help us push the boundaries of embodied AI.