arXiv AI
Hiking in the Wild: A Scalable Perceptive Parkour Framework for Humanoids
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arXiv Machine Learning
Jun 9
Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking
arXiv:2606. 08253v1 Announce Type: cross Abstract: Enabling humanoid robots to operate in complex, dynamic environments remains a critical challenge, fundamentally limited by the ability to navigate robustly, safely, and accurately.
arXiv Machine Learning
Oct 22
Watch Your Step: Anticipated Perception for Humanoid Locomotion
The paper proposes a lightweight foothold prediction model (FPM) that anticipates future foot placements using only proprioception and velocity commands, allowing the robot to sample elevation maps only around predicted footholds. This preprocessing reduces redundant sensory input, enabling the locomotion policy to learn faster—reaching a given terrain difficulty in about one‑third the training iterations compared to using the full elevation map. The resulting policy outperforms both full‑map and attention‑based encoders on sparse foothold scenarios, and it zero‑shot transfers to a Unitree G1 humanoid across diverse indoor and outdoor obstacles.
arXiv AI
Oct 28
REACH: An RL-based Hierarchical Humanoid Crawling Framework for Adaptable Confined Space Traversal
REACH is a reinforcement‑learning based hierarchical framework that allows a Unitree G1 humanoid robot to navigate confined spaces by switching between variable‑height bipedal walking and crawling. The system learns policies that adjust body height from 1.27 m down to 0.20 m, uses a history‑conditioned latent representation to handle contact‑rich crawling dynamics, and incorporates a bidirectional transition policy to connect the two locomotion modes. A LiDAR‑based planner generates collision‑free 3D paths and proactive height targets, and real‑world tests confirm accurate height and velocity tracking, robust terrain traversal, and reliable mode transitions.
arXiv AI
Jul 14
PUMA: Perception-driven Unified Foothold Prior for Mobility Augmented Quadruped Parkour
arXiv:2601. 15995v2 Announce Type: replace-cross Abstract: Parkour tasks for quadrupeds have emerged as a promising benchmark for agile locomotion.
arXiv AI
Oct 15
TTT-Parkour: Rapid Test-Time Training for Perceptive Robot Parkour
arXiv:2602.02331v2 Announce Type: replace-cross Abstract: Achieving highly dynamic parkour with humanoid robots on complex terrains unseen during the pre-training stage remains a challenge in robotic...
arXiv Machine Learning
Jun 16
LoComposition: Terrain-Adaptive Energy-Efficient Quadruped Locomotion without Gait Priors
arXiv:2606. 15896v1 Announce Type: cross Abstract: Learning-based quadrupedal locomotion typically relies on complex reward formulations that entangle task specification, operational limits, gait preference, and terrain adaptation within a single optimization objective.