Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learning

• URL: http://arxiv.org/abs/2408.14472v1
• Date: Mon, 26 Aug 2024 17:59:03 GMT
• Title: Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learning
• Authors: Xinyang Gu, Yen-Jen Wang, Xiang Zhu, Chengming Shi, Yanjiang Guo, Yichen Liu, Jianyu Chen,
• Abstract summary: We introduce Denoising World Model Learning (DWL), an end-to-end reinforcement learning framework for humanoid locomotion control. DWL demonstrates the world's first humanoid robot to master real-world challenging terrains such as snowy and inclined land in the wild, up and down stairs, and extremely uneven terrains.
• Score: 11.648198428063415
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humanoid robots, with their human-like skeletal structure, are especially suited for tasks in human-centric environments. However, this structure is accompanied by additional challenges in locomotion controller design, especially in complex real-world environments. As a result, existing humanoid robots are limited to relatively simple terrains, either with model-based control or model-free reinforcement learning. In this work, we introduce Denoising World Model Learning (DWL), an end-to-end reinforcement learning framework for humanoid locomotion control, which demonstrates the world's first humanoid robot to master real-world challenging terrains such as snowy and inclined land in the wild, up and down stairs, and extremely uneven terrains. All scenarios run the same learned neural network with zero-shot sim-to-real transfer, indicating the superior robustness and generalization capability of the proposed method.

Related papers

• Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics [50.191655141020505]
  We introduce a novel framework for learning world models. By providing a scalable and robust framework, we pave the way for adaptive and efficient robotic systems in real-world applications.
  arXiv  Detail & Related papers  (2025-01-17T10:39:09Z)
• Human-Humanoid Robots Cross-Embodiment Behavior-Skill Transfer Using Decomposed Adversarial Learning from Demonstration [9.42179962375058]
  We propose a transferable framework that reduces the data bottleneck by using a unified digital human model as a common prototype. The model learns behavior primitives from human demonstrations through adversarial imitation, and complex robot structures are decomposed into functional components. Our framework is validated on five humanoid robots with diverse configurations.
  arXiv  Detail & Related papers  (2024-12-19T18:41:45Z)
• Learning from Massive Human Videos for Universal Humanoid Pose Control [46.417054298537195]
  This paper introduces Humanoid-X, a large-scale dataset of over 20 million humanoid robot poses with corresponding text-based motion descriptions. We train a large humanoid model, UH-1, which takes text instructions as input and outputs corresponding actions to control a humanoid robot. Our scalable training approach leads to superior generalization in text-based humanoid control, marking a significant step toward adaptable, real-world-ready humanoid robots.
  arXiv  Detail & Related papers  (2024-12-18T18:59:56Z)
• Learning Humanoid Locomotion over Challenging Terrain [84.35038297708485]
  We present a learning-based approach for blind humanoid locomotion capable of traversing challenging natural and man-made terrains. Our model is first pre-trained on a dataset of flat-ground trajectories with sequence modeling, and then fine-tuned on uneven terrain using reinforcement learning. We evaluate our model on a real humanoid robot across a variety of terrains, including rough, deformable, and sloped surfaces.
  arXiv  Detail & Related papers  (2024-10-04T17:57:09Z)
• Hierarchical generative modelling for autonomous robots [8.023920215148486]
  We show how a humanoid robot can autonomously complete a complex task that requires a holistic use of locomotion, manipulation, and grasping. Specifically, we demonstrate the ability of a humanoid robot that can retrieve and transport a box, open and walk through a door to reach the destination, approach and kick a football, while showing robust performance in presence of body damage and ground irregularities.
  arXiv  Detail & Related papers  (2023-08-15T13:51:03Z)
• Learning Human-to-Robot Handovers from Point Clouds [63.18127198174958]
  We propose the first framework to learn control policies for vision-based human-to-robot handovers. We show significant performance gains over baselines on a simulation benchmark, sim-to-sim transfer and sim-to-real transfer.
  arXiv  Detail & Related papers  (2023-03-30T17:58:36Z)
• Real-World Humanoid Locomotion with Reinforcement Learning [92.85934954371099]
  We present a fully learning-based approach for real-world humanoid locomotion. Our controller can walk over various outdoor terrains, is robust to external disturbances, and can adapt in context.
  arXiv  Detail & Related papers  (2023-03-06T18:59:09Z)
• Learning Quadrupedal Locomotion over Challenging Terrain [68.51539602703662]
  Legged locomotion can dramatically expand the operational domains of robotics. Conventional controllers for legged locomotion are based on elaborate state machines that explicitly trigger the execution of motion primitives and reflexes. Here we present a radically robust controller for legged locomotion in challenging natural environments.
  arXiv  Detail & Related papers  (2020-10-21T19:11:20Z)
• Visual Navigation Among Humans with Optimal Control as a Supervisor [72.5188978268463]
  We propose an approach that combines learning-based perception with model-based optimal control to navigate among humans. Our approach is enabled by our novel data-generation tool, HumANav. We demonstrate that the learned navigation policies can anticipate and react to humans without explicitly predicting future human motion.
  arXiv  Detail & Related papers  (2020-03-20T16:13:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.