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ADAPT: Adaptive Dual-projection Architecture for Perceptive Traversal

Shuo Shao, Tianchen Huang, Wei Gao, Shiwu Zhang

Abstract

Agile humanoid locomotion in complex 3D en- vironments requires balancing perceptual fidelity with com- putational efficiency, yet existing methods typically rely on rigid sensing configurations. We propose ADAPT (Adaptive dual-projection architecture for perceptive traversal), which represents the environment using a horizontal elevation map for terrain geometry and a vertical distance map for traversable- space constraints. ADAPT further treats its spatial sensing range as a learnable action, enabling the policy to expand its perceptual horizon during fast motion and contract it in cluttered scenes for finer local resolution. Compared with voxel-based baselines, ADAPT drastically reduces observation dimensionality and computational overhead while substantially accelerating training. Experimentally, it achieves successful zero-shot transfer to a Unitree G1 Humanoid and signifi- cantly outperforms fixed-range baselines, yielding highly robust traversal across diverse 3D environtmental challenges.

ADAPT: Adaptive Dual-projection Architecture for Perceptive Traversal

Abstract

Agile humanoid locomotion in complex 3D en- vironments requires balancing perceptual fidelity with com- putational efficiency, yet existing methods typically rely on rigid sensing configurations. We propose ADAPT (Adaptive dual-projection architecture for perceptive traversal), which represents the environment using a horizontal elevation map for terrain geometry and a vertical distance map for traversable- space constraints. ADAPT further treats its spatial sensing range as a learnable action, enabling the policy to expand its perceptual horizon during fast motion and contract it in cluttered scenes for finer local resolution. Compared with voxel-based baselines, ADAPT drastically reduces observation dimensionality and computational overhead while substantially accelerating training. Experimentally, it achieves successful zero-shot transfer to a Unitree G1 Humanoid and signifi- cantly outperforms fixed-range baselines, yielding highly robust traversal across diverse 3D environtmental challenges.
Paper Structure (30 sections, 3 equations, 3 figures, 9 tables)

This paper contains 30 sections, 3 equations, 3 figures, 9 tables.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. Perception for Legged Locomotion
  4. Adaptive Perception and Behavior Modulation
  5. METHOD
  6. Problem Formulation
  7. Training Pipeline
  8. Observation Space
  9. Action Space
  10. Reward Functions
  11. Terrain Design
  12. Training Architecture
  13. Experiment
  14. Experimental Configuration
  15. Simulation Environment
  16. ...and 15 more sections

Figures (3)

  • Figure 1: The proposed training architecture separates exteroceptive and proprioceptive inputs. Two MLP encoders process the perception maps independently. Their features are then fused with the proprioceptive state and passed to a GRU-based recurrent actor-critic. The actor predicts both the joint targets and the perception radius for the next step. The critic uses privileged, noise-free simulator information to produce a more accurate value estimate and stabilize training.
  • Figure 2: Illustration of the adaptive perception radius mechanism. The vertical distance map is constructed on a cylindrical sector with a fixed number of cells ($17 \times 13$). A smaller radius (blue) provides higher spatial resolution for precise near-field sensing, while a larger radius (purple) extends the perception range at the cost of reduced resolution. The policy learns to dynamically adjust this radius based on the robot's locomotion state.
  • Figure 3: Examples of procedurally generated obstacles in our BarrierTrack training environment. The curriculum includes a diverse set of challenges such as stairs, gaps, hurdles, and constrained passages like beams and narrow gates, promoting the development of a versatile locomotion policy.