- The paper presents the Perceptive Internal Model that integrates LiDAR-based elevation maps to enhance humanoid locomotion by addressing inherent instability and complex terrain challenges.
- It employs a Hybrid Internal Model trained in simulation with ground-truth obstacle heights, achieving robust navigation across diverse indoor and outdoor environments.
- The research demonstrates impressive computational efficiency, completing training in three hours on an RTX 4090 GPU and setting a benchmark for sim-to-real transitions in robotics.
Learning Humanoid Locomotion with Perceptive Internal Model
The paper "Learning Humanoid Locomotion with Perceptive Internal Model" addresses the complex problem of achieving stable and dynamic locomotion in humanoid robots, which possess high degrees of freedom and inherently unstable morphologies. Unlike quadruped robots that can rely on a "blind" policy to traverse various terrains, humanoid robots necessitate precise perception for stable and adaptive movement. The introduction of perceptual signals, however, can introduce additional disturbances and complexities, potentially reducing system robustness and efficiency.
This research proposes the Perceptive Internal Model (PIM), a novel approach that uses onboard, continuously updated elevation maps for terrain perception around the robot. The PIM, trained in a simulated environment using ground-truth obstacle heights, integrates perceptive information to optimize robot locomotion through a simulated internal model framework known as the Hybrid Internal Model (HIM). This strategy contrasts with previous efforts that encode raw point clouds or depth maps, which are more susceptible to camera noise and movement disturbances. The methodology employed by PIM involves training in simulation with ground-truth mappings and uses LiDAR-based elevation maps for perception during real-world inference, which allows for substantial computational efficiency.
The results presented in the paper are notable in terms of computational efficiency, highlighting that the training process can be completed within three hours on an RTX 4090 GPU. This efficiency is achieved without the need for depth map rendering in simulation, thereby avoiding additional computational costs. The outcomes demonstrate the method's robustness across a variety of humanoid robots and terrains, including both indoor and outdoor environments, and validate its ability to enable continuous stair climbing at a high success rate.
The implications of this research are significant for the field of humanoid robotics. By effectively integrating perceptive information into locomotion policy, this work lays the groundwork for future developments in humanoid control methods, offering a more reliable foundation for robots to handle complex and dynamic environments. The PIM has the potential to ameliorate existing challenges in perceptive legged locomotion, providing a framework that can support the development of more agile and adaptive humanoid robots.
Future developments stemming from this work may lead to enhancements in humanoid robotic navigation and task execution in various real-world settings. The research opens avenues for further exploration into sim2real transitions, where the fidelity of simulation to real-world performance can be fine-tuned and optimized beyond the robust results already achieved in this paper. Additionally, the exploitation of more complex sensor fusion strategies and machine perception technologies can be anticipated as a natural extension of the foundational methodologies introduced here. This research offers profound insights and concrete methods that can be adopted and adapted by robotics researchers aiming to push the boundaries of humanoid robot capabilities.