Advancements in Legged Robot Navigation

Legged robots are designed to move across different types of surfaces, and this is not an easy task. They need to really understand their own movements and what is happening around them. This "understanding" comes from two main sources: their own sense of position and movement, and what they see through cameras. However, using images from cameras to learn how to move is often slow and requires a lot of data.

To tackle this challenge, some traditional methods first teach a robot (the teacher) with lots of detailed information, and then another robot (the student) tries to copy how the teacher moves by looking only at pictures. While this method shows some improvements, the student robot often does not perform as well as it could. This is because the student robot does not get all the information the teacher robot has, making it harder for the student to learn effectively. Besides, when animals learn to walk on different surfaces, they do so naturally without needing special information ahead of time.

Inspired by how animals learn, a new method called World Model-based Perception (WMP) is proposed. This method builds a model of the world around the robot and teaches it how to move based on that model. The world model is trained in a computer simulation, allowing it to make accurate predictions about what will happen in the real world. This helps the robot understand its surroundings better and make informed decisions.

#Challenges of Legged Locomotion

Moving on different surfaces can be tough for legged robots. They often encounter slopes, stairs, gaps, and other obstacles that require them to perceive their environment correctly. While a robot can navigate some terrains using its sense of position and movement alone, it struggles with more challenging ones, like gaps or pits, where it needs to see the terrain in advance. Thus, visual input is crucial for effective locomotion.

Learning to move based solely on camera images can be very slow and requires a lot of experiences. When using a camera directed forward, a robot must remember what it has seen in the past to figure out what is directly below it. This situation makes the learning process challenging.

To help with this, some methods introduce a special learning framework. In this framework, a teacher robot learns with access to basic information like special points around it. Then, the student robot tries to copy the teacher by looking at images. However, this approach has some drawbacks. For instance, the student robot may not perfectly imitate the teacher's movements, and the performance can fall short, especially when there is a gap in knowledge between the teacher and student.

#Natural Learning in Animals

Animals, including humans, learn to move across various environments naturally. They build mental models of their surroundings and make decisions based on their understanding. When they perform actions, these models help them anticipate what will happen next. This instinctive behavior helps them traverse unknown terrain even with limited information.

Model-Based Reinforcement Learning (MBRL) takes inspiration from this natural learning process. It involves developing a world model based on data collected during the robot's training. This model helps in decision-making and allows the robot to deal with different tasks efficiently.

#The World Model-based Perception (WMP) Framework

The WMP framework combines MBRL with legged locomotion that relies on vision. The framework trains a world model using simulations, enabling the robot to predict what it will perceive in the future based on past experiences. The policy, or the robot's instructions on how to move, is derived from this world model. Even after being trained only in simulations, the model can still accurately predict how the robot will behave in the real world.

By using the learned world model, WMP overcomes some of the limitations present in traditional learning methods. It condenses vast amounts of visual information into a simpler form, making it easier for the robot to make decisions.

#Experimenting with WMP

Various experiments have been conducted to see how WMP performs compared to other advanced methods. The experiments included a range of terrains with differing levels of difficulty. Results showed that WMP earned very high rewards in simulations, indicating effective performance.

The ability of WMP to function well in real-life tests was also evaluated. The WMP method was implemented on a robot called the Unitree A1, which was able to navigate through tested terrains with remarkable success, even with greater challenges than anticipated.

For instance, the WMP method enabled the robot to traverse significant gaps and climb obstacles that were taller than itself. These successes indicate that WMP has an edge when it comes to real-world locomotion compared to its predecessors.

#Comparing WMP with Other Methods

WMP was compared to methods that used only Proprioception, which is the robot's sense of its own position and movement, without visual input. While other methods showed some ability to navigate simpler terrains, they did not perform well in more complex environments. WMP, on the other hand, exhibited superior success, demonstrating more consistent behavior and adaptability to various types of challenging surfaces.

The experiments also involved evaluating the effect of the model interval, which is the time between updates of the world model. The results indicated that models with shorter intervals generally performed better, as they allowed for faster responses to changes in the environment. However, a balance was needed between ideal performance and computational costs.

#Training the World Model

To train the world model, a robotic system was set up to simulate multiple robots exploring different terrains simultaneously. Training involved creating various terrain types, ensuring that each robot experienced a range of challenges. The robots learned to respond to their environments, gradually improving their ability to navigate from basic to more complex tasks.

#Real-World Application and Evaluation

The WMP method was also tested in real-world settings. The robots were put to the test in outdoor environments, traversing stairs, climbing, and crossing uneven ground, demonstrating their adaptability in various conditions. These evaluations showed consistent behavior across different terrains, confirming that the robots could effectively transfer skills learned in simulations to real-world scenarios.

#Conclusion

In conclusion, World Model-based Perception (WMP) offers a promising framework for improving the way legged robots navigate complex environments through the combination of simulated world modeling and visual input. By learning from past experiences and building a mental model of their surroundings, robots can make informed decisions and adapt to various terrains effectively. This method shows great potential for advancing robot control and could pave the way for enhancements in how robots learn to move naturally.

Future work aims to incorporate real-world data along with simulated data to further refine the world model. Additionally, expanding the model to include other sensory inputs may enhance the robot's performance even more, providing a broader scope for applications.