#Local Navigation (3)

ScaleNav: Scalable Internet Video Pretraining for Language-Conditioned Local Navigation

ScaleNav studies language-conditioned local navigation, where a robot must reach a text-specified target that is visible in its initial observation and stop near the described location. The method starts from action-free Internet egocentric videos, learns how observations evolve as a camera approaches an instructed target, aligns the resulting visual priors with executable action chunks using action-labeled simulation, and then jointly fine-tunes video and action prediction. The authors report 86.5% success on a single-view Short-Horizon OVON adaptation, 94.9% on HabitatGS-LN, and 83.3% overall success across 60 real-robot trials on a Unitree Go2 in three indoor environments.

Yihui Yang, Yuanhong Yu, Jiawei Wang · 71 views · Oct 19, 2026

Local NavigationVision-language-action (VLA)

Oct 19, 2026

Motion Priors Reimagined: Adapting Flat-Terrain Skills for Complex Quadruped Mobility

Reinforcement learning (RL)-based motion imitation methods trained on demonstration data can effectively learn natural and expressive motions with minimal reward engineering but often struggle to generalize to novel environments. We address this by proposing a hierarchical RL framework in which a low-level policy is first pre-trained to imitate animal motions on flat ground, thereby establishing motion priors. A subsequent high-level, goal-conditioned policy then builds on these priors, learning residual corrections that enable perceptive locomotion, local obstacle avoidance, and goal-directed navigation across diverse and rugged terrains. Simulation experiments illustrate the effectiveness of learned residuals in adapting to progressively challenging uneven terrains while still preserving the locomotion characteristics provided by the motion priors. Furthermore, our results demonstrate improvements in motion regularization over baseline models trained without motion priors under similar reward setups. Real-world experiments with an ANYmal-D quadruped robot confirm our policy's capability to generalize animal-like locomotion skills to complex terrains, demonstrating smooth and efficient locomotion and local navigation performance amidst challenging terrains with obstacles.

Zewei Zhang, Chenhao Li, Takahiro Miki · 619 views · May 21, 2025

ANYmalQuadruped robots

May 21, 2025

Gallant: Voxel Grid-based Humanoid Locomotion and Local-navigation across 3D Constrained Terrains

Robust humanoid locomotion requires accurate and globally consistent perception of the surrounding 3D environment. However, existing perception modules, mainly based on depth images or elevation maps, offer only partial and locally flattened views of the environment, failing to capture the full 3D structure. This paper presents Gallant, a voxel-grid-based framework for humanoid locomotion and local navigation in 3D constrained terrains. It leverages voxelized LiDAR data as a lightweight and structured perceptual representation, and employs a z-grouped 2D CNN to map this representation to the control policy, enabling fully end-to-end optimization. A high-fidelity LiDAR simulation that dynamically generates realistic observations is developed to support scalable, LiDAR-based training and ensure sim-to-real consistency. Experimental results show that Gallant's broader perceptual coverage facilitates the use of a single policy that goes beyond the limitations of previous methods confined to ground-level obstacles, extending to lateral clutter, overhead constraints, multi-level structures, and narrow passages. Gallant also firstly achieves near 100% success rates in challenging scenarios such as stair climbing and stepping onto elevated platforms through improved end-to-end optimization.

Qingwei Ben, Botian Xu, Kailin Li · 428 views · Nov 18, 2025

HumanoidVoxel Grid

Nov 18, 2025

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