Can a humanoid learn faster and better by seeing less?
A research paper explores whether a humanoid robot can learn to navigate challenging terrain more effectively by limiting its visual input to only the immediate area ahead. The study shows that training a policy model with reduced visual data can still enable the robot to cross gaps, stairs, drops, and boxes.
The paper investigates whether reducing the visual input for a humanoid robot can improve its locomotion performance, suggesting that focusing on immediate terrain ahead may enhance efficiency in navigating complex environments.
Source: ETH Robotic Systems Lab — X · Read original article ↗
Article text · Original source · English
Can a humanoid learn faster and better by seeing less?
We train a small model to predict the policy's next steps, and show the policy only the terrain there. Together, they learn to cross gaps, stairs, drops and boxes.
🌐 https://leggedrobotics.github.io/watch-your-step
📄 https://arxiv.org/abs/2610.29612
What does a humanoid need to look at to cross challenging terrain? People only look around their next few planned steps. So we showed a humanoid only that. Watch Your Step: Anticipated Perception for Humanoid Locomotion 🌐 https://leggedrobotics.github.io/watch-your-step 📄 https://arxiv.org/abs/2610.29612View the quoted post on X
Source：Robotic Systems Lab · x.com