# CAP: Continuously Adaptive Perception-Blind Humanoid Locomotion via Learned Denoising

arXiv: https://arxiv.org/abs/2609.11553 · HTML: https://mlsanity.com/paper/2609.11553/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Humanoid and legged locomotion world models, one of 11 directions in World models.
- Builds on 13 of its 42 references that are in the map.
- First posted on arXiv 10 Sep 2026.

## Placed in

- World models / [Humanoid and legged locomotion world models](https://mlsanity.com/wm/humanoid-and-legged-locomotion-world-models/)

## Builds on (most cited first, 13 of 13)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching](https://mlsanity.com/paper/2602.15827/) (2026)
- [Gallant: Voxel Grid-based Humanoid Locomotion and Local-navigation across 3D Constrained Terrains](https://mlsanity.com/paper/2511.14625/) (2025)
- [Hiking in the Wild: A Scalable Perceptive Parkour Framework for Humanoids](https://mlsanity.com/paper/2601.07718/) (2026)
- [DPL: Depth-only Perceptive Humanoid Locomotion via Realistic Depth Synthesis and Cross-Attention Terrain Reconstruction](https://mlsanity.com/paper/2510.07152/) (2025)
- [MoRE: Mixture of Residual Experts for Humanoid Lifelike Gaits Learning on Complex Terrains](https://mlsanity.com/paper/2506.08840/) (2025)
- [RPL: Learning Robust Humanoid Perceptive Locomotion on Challenging Terrains](https://mlsanity.com/paper/2602.03002/) (2026)
- [Learning Whole-Body Humanoid Locomotion via Motion Generation and Motion Tracking](https://mlsanity.com/paper/2604.17335/) (2026)
- [Walk the PLANC: Physics-Guided RL for Agile Humanoid Locomotion on Constrained Footholds](https://mlsanity.com/paper/2601.06286/) (2026)
- [Now You See That: Learning End-to-End Humanoid Locomotion from Raw Pixels](https://mlsanity.com/paper/2602.06382/) (2026)
- [APEX: Learning Adaptive High-Platform Traversal for Humanoid Robots](https://mlsanity.com/paper/2602.11143/) (2026)
- [CMoE: Contrastive Mixture of Experts for Motion Control and Terrain Adaptation of Humanoid Robots](https://mlsanity.com/paper/2603.03067/) (2026)
- [REAL: Robust Extreme Agility via Spatio-Temporal Policy Learning and Physics-Guided Filtering](https://mlsanity.com/paper/2603.17653/) (2026)

## Abstract (arXiv)

Humanoid locomotion across complex terrain demands forward-looking exteroception to anticipate obstacles, yet this signal is unreliable in real-world deployment, failing partially and intermittently. Existing perceptive policies often assume that depth observations remain clean and in-distribution, while recent attempts to unify perceptive and blind control typically route or switch between separate sub-policies, leaving recoverable information in partially corrupted depth unexploited. We instead propose CAP, a single-stage humanoid locomotion policy that recovers this signal with a perceptive world-model encoder trained as a learned denoiser to reconstruct clean depth from a corrupted input, together with a co-active proprioceptive variational encoder that supplies depth-free body-state information. A coupled training recipe pairs a depth-noise curriculum on the world-model input with world-model feature dropout on the policy-facing latent, exposing the policy to failures across the entire perception-quality spectrum. In simulation, CAP matches or improves upon perceptive baselines when depth remains informative, and degrades more smoothly than a binary-switching baseline as perception worsens. On the Unitree G1, controlled trials and indoor-outdoor deployments demonstrate perception-robust locomotion under intermittent occlusion, real-sensor corruption, and outdoor depth artifacts.