Okay, let's break down why reinforcement learning (RL) is favored for robot dog locomotion over relying solely on machine vision for foot placement:

Why Reinforcement Learning for Walking?

• Dealing with Complexity: High-Dimensional Control: Walking involves coordinating many joints and actuators in a complex, dynamic way. Manually programming this is extremely difficult and time-consuming. Unpredictable Environments: Real-world environments are uneven, slippery, and have obstacles. Pre-programmed walking patterns would likely fail.
• Adaptability and Robustness: Learning from Experience: RL allows the robot to learn a robust gait by trying different actions, learning from successes and failures, and adapting to the environment. Handling Uncertainty: It's very difficult to predict the exact physics of the robot or the environment's characteristics perfectly. RL can overcome this by learning from real-world experience.
• Emergent Behavior: RL can lead to the discovery of surprisingly efficient and elegant gaits that would be difficult to manually design.
• Automated Training: Once the RL framework is set up, the robot can train autonomously (usually in simulation), requiring less human intervention.
• Optimizing for Multiple Objectives: RL can be set up to optimize several objectives simultaneously, like speed, stability, and energy efficiency.

Why Machine Vision is Insufficient for Direct Foot Placement (On its Own)?

• Limited Information: Missing Dynamics: Vision only gives a snapshot of the current environment. It doesn't know about the robot's internal state, velocity, or inertia, or how its body will react to certain actions. This is critical to controlling a dynamic system like a robot dog. Depth Perception Challenges: Getting accurate depth information from vision alone, especially in cluttered or changing scenes, can be unreliable for precision tasks. Occlusion: The vision system might not be able to see the optimal landing point due to occlusions.
• Latency: Processing Time: Processing visual information takes time. By the time the vision system determines where to step, the robot might have moved, or the terrain may have changed. This delay can lead to instability.
• Complexity of mapping vision to actions: It is hard to map a visual scene to the exact joint movements needed to perform a step on an uneven ground while maintaining balance. It is complex to create an inverse model that perfectly connects the visual information to motor control. Environmental Generalization: A vision-based controller may not generalize well to different environment conditions (different lighting, different terrains, etc.)

The Role of Vision

It's important to note that vision is not useless for robot locomotion. Vision can and is often used in conjunction with RL:

• Providing Context: Vision can provide the robot with information about its environment (e.g., terrain type, obstacles, distance to goal) that can inform the RL controller about the "state" or "context".
• Path Planning: Vision can be used for global path planning - i.e., the route that a robot is going to take. However, the detailed foot placement is still handled by a local controller.
• Terrain Awareness: Using computer vision the robot can make a decision about whether it needs to take the stairs or move around the obstacle.

In short:

• RL is the brain: RL provides the core mechanism to learn the complex coordination for walking using feedback and trial-and-error.
• Vision is an eye: Vision is great for perceiving the environment and can influence the RL controller to inform decision making by providing environmental context.
• Combined: The most effective robot control often combines vision and reinforcement learning. Vision provides information about the environment and the goals. RL, learns the specific low-level motor control that is necessary for a particular task.

While you could, in theory, program rules to map a visual scene to leg movements, the system would likely be brittle and struggle with real-world complexities. RL offers a far more powerful way to create robust and adaptive locomotion.