TAGA: Terrain-aware Active Gaze Learning for Generalizable Agile Humanoid Locomotion

• URL: http://arxiv.org/abs/2606.05880v1
• Date: Thu, 04 Jun 2026 08:52:56 GMT
• Title: TAGA: Terrain-aware Active Gaze Learning for Generalizable Agile Humanoid Locomotion
• Abstract summary: We introduce TAGA, a Terrain-aware Active Gaze learning framework for Attention-based humanoid control.<n>Our framework guides the model to learn anticipatory cues and actively attend to specific areas of the height scan.<n>We find that such gaze behaviors can naturally emerge through reinforcement learning alone, without requiring additional supervision or explicit guidance.
• Score: 13.927597120628064
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Agile humanoid locomotion across diverse challenging terrain demands both wide perceptual coverage and precise local geometry understanding. Motivated by the way humans selectively look at relevant terrain during locomotion, we introduce TAGA, a Terrain-aware Active Gaze learning framework for Attention-based humanoid control. By fusing vision, proprioception, and motion commands, our framework guides the model to learn anticipatory cues and actively attend to specific areas of the height scan, selectively using these informative regions for the downstream network. This adaptively increases the information density of observations under tight onboard computational constraints, thus enabling fine-grained perceptive locomotion over larger-scale terrains. We find that such gaze behaviors can naturally emerge through reinforcement learning alone, without requiring additional supervision or explicit guidance, significantly improve training efficiency. As a result, the trained policy demonstrates robust and generalizable locomotion in simulation and on hardware, including reliable terrain-aware foothold selection, elevated-platform traversal, competitive sparse-foothold traversal, and the largest reported real-world gap traversal distance of 1.2m among perceptive humanoid locomotion systems, while maintaining stability under severe perceptual disturbances and environmental interference.

Related papers

• Global-Local Attention Decomposition for Terrain Encoding in Humanoid Perceptive Locomotion [7.888727249239356]
  We propose Global-Local Attention Decomposition (GLAD) for terrain encoding in humanoid locomotion.<n> Experiments demonstrate that GLAD enables reliable locomotion over challenging gaps, stepping stones, and stairs.<n>In real-world deployment on a Unitree G1 humanoid robot using onboard LiDAR, the proposed method achieves robust zero-shot sim-to-real transfer.
  arXiv  Detail & Related papers  (2026-05-30T09:23:07Z)
• Learning Whole-Body Humanoid Locomotion via Motion Generation and Motion Tracking [22.8041193953823]
  Whole-body humanoid locomotion is challenging due to high-dimensional control, morphological instability, and the need for real-time adaptation to various terrains using onboard perception.<n>We propose a whole-body humanoid locomotion framework that combines skills learned from reference motions with terrain-aware adaptation.
  arXiv  Detail & Related papers  (2026-04-19T09:02:55Z)
• Gait-Adaptive Perceptive Humanoid Locomotion with Real-Time Under-Base Terrain Reconstruction [86.53248703859718]
  We introduce a perceptive locomotion framework that merges terrain sensing, gait regulation, and whole-body control into a single reinforcement learning policy.<n>Experiments conducted on a 31-DoF, 1.65 m humanoid robot demonstrate robust locomotion in both simulation and real-world settings.
  arXiv  Detail & Related papers  (2025-12-08T11:42:41Z)
• No More Blind Spots: Learning Vision-Based Omnidirectional Bipedal Locomotion for Challenging Terrain [12.51464645002418]
  We present a learning framework for vision-based omnidirectional bipedal locomotion.<n>A key challenge is the high computational cost of rendering omnidirectional depth images in simulation.<n>Our method combines a robust blind controller with a teacher policy that supervises a vision-based student policy.
  arXiv  Detail & Related papers  (2025-08-16T06:20:46Z)
• Open-World Drone Active Tracking with Goal-Centered Rewards [62.21394499788672]
  Drone Visual Active Tracking aims to autonomously follow a target object by controlling the motion system based on visual observations.<n>We propose DAT, the first open-world drone active air-to-ground tracking benchmark.<n>We also propose GC-VAT, which aims to improve the performance of drone tracking targets in complex scenarios.
  arXiv  Detail & Related papers  (2024-12-01T09:37:46Z)
• Universal Humanoid Motion Representations for Physics-Based Control [71.46142106079292]
  We present a universal motion representation that encompasses a comprehensive range of motor skills for physics-based humanoid control. We first learn a motion imitator that can imitate all of human motion from a large, unstructured motion dataset. We then create our motion representation by distilling skills directly from the imitator.
  arXiv  Detail & Related papers  (2023-10-06T20:48:43Z)
• Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion [83.88829943619656]
  We introduce a method for generating realistic pedestrian trajectories and full-body animations that can be controlled to meet user-defined goals. Our guided diffusion model allows users to constrain trajectories through target waypoints, speed, and specified social groups. We propose utilizing the value function learned during RL training of the animation controller to guide diffusion to produce trajectories better suited for particular scenarios.
  arXiv  Detail & Related papers  (2023-04-04T15:46:42Z)
• Learning to Jump from Pixels [23.17535989519855]
  We present Depth-based Impulse Control (DIC), a method for synthesizing highly agile visually-guided behaviors. DIC affords the flexibility of model-free learning but regularizes behavior through explicit model-based optimization of ground reaction forces. We evaluate the proposed method both in simulation and in the real world.
  arXiv  Detail & Related papers  (2021-10-28T17:53:06Z)
• Learning Perceptual Locomotion on Uneven Terrains using Sparse Visual Observations [75.60524561611008]
  This work aims to exploit the use of sparse visual observations to achieve perceptual locomotion over a range of commonly seen bumps, ramps, and stairs in human-centred environments. We first formulate the selection of minimal visual input that can represent the uneven surfaces of interest, and propose a learning framework that integrates such exteroceptive and proprioceptive data. We validate the learned policy in tasks that require omnidirectional walking over flat ground and forward locomotion over terrains with obstacles, showing a high success rate.
  arXiv  Detail & Related papers  (2021-09-28T20:25:10Z)
• Learning to Move with Affordance Maps [57.198806691838364]
  The ability to autonomously explore and navigate a physical space is a fundamental requirement for virtually any mobile autonomous agent. Traditional SLAM-based approaches for exploration and navigation largely focus on leveraging scene geometry. We show that learned affordance maps can be used to augment traditional approaches for both exploration and navigation, providing significant improvements in performance.
  arXiv  Detail & Related papers  (2020-01-08T04:05:11Z)

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.