Cross Any Terrain with Your RoboDog 🦮
So essentially,
Foundational Models for Quadruped Robot Navigation have the most robust performance!
Paper:
Cross Anything: General Quadruped Robot Navigation through Complex Terrains (17 Pages)
Github: https://cross-anything.github.io/
Researchers from Shanghai Qi Zhi Institute, Zhejiang University, Shanghai Jiao Tong University, Tsinghua University are interested in developing foundation models used in quadruped robot navigation.
Hmm..What’s the background?
Previous research applied foundation models, including large language models (LLMs) and VLMs, to robotics, but were limited to planar surfaces and didn't utilize the full 3D complex terrain capabilities of quadruped robots.
This paper introduces CAS, a novel system that enables quadruped robots to navigate complex 3D terrains by leveraging the power of Vision-Language Models (VLMs) for high-level reasoning and a novel reinforcement learning-based locomotion control policy for robust movement.
Ok, So what is proposed in the research paper?
The paper has the following key proposals:
CAS is an innovative system designed for general quadruped robot navigation through complex 3D terrains
The motivation behind developing CAS stemmed from the limitations of vision-language models (VLMs) in robot navigation tasks, specifically due to limited training data perspectives and a lack of a memory bank
This mechanism breaks down complex navigation tasks into smaller, manageable sub-tasks. By leveraging a VLM, CAS can analyze its environment, identify obstacles, and plan a sequence of sub-tasks to reach a target
The experiments focused on testing the system's performance across a variety of challenging terrains, including stairs, ramps, gaps, and uneven surfaces
Source: Github
What’s next?
The researchers share potential areas for improvement:
The current Simultaneous Localization and Mapping (SLAM) method, though commonly used, lacks stability due to high-frequency robot vibrations
Unlike other navigation systems like ViNT, CAS currently lacks an integrated memory component, such as a topological map
Robot's understanding of its environment, derived from the maps, can enhance the VLM's reasoning and planning capabilities
So essentially,
Foundational Models for Quadruped Robot Navigation have the most robust performance!