#Robotics (26)

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DeFM: Learning Foundation Representations from Depth for Robotics

Depth sensors are widely deployed across robotic platforms, and advances in fast, high-fidelity depth simulation have enabled robotic policies trained on depth observations to achieve robust sim-to-real transfer for a wide range of tasks. Despite this, representation learning for depth modality remains underexplored compared to RGB, where large-scale foundation models now define the state of the art. To address this gap, we present DeFM, a self-supervised foundation model trained entirely on depth images for robotic applications. Using a DINO-style self-distillation objective on a curated dataset of 60M depth images, DeFM learns geometric and semantic representations that generalize to diverse environments, tasks, and sensors. To retain metric awareness across multiple scales, we introduce a novel input normalization strategy. We further distill DeFM into compact models suitable for resource-constrained robotic systems. When evaluated on depth-based classification, segmentation, navigation, locomotion, and manipulation benchmarks, DeFM achieves state-of-the-art performance and demonstrates strong generalization from simulation to real-world environments. We release all our pretrained models, which can be adopted off-the-shelf for depth-based robotic learning without task-specific fine-tuning. Webpage: https://de-fm.github.io/

Manthan Patel, Jonas Frey, Mayank Mittal · 127 views · Jan 26, 2026

DepthSelf-Supervised Learning

Jan 26, 2026

StageVLN: Spatial and Trajectory Auxiliary Guidance for Efficient Vision-Language Navigation

Vision-and-Language Navigation (VLN) policies increasingly benefit from strong semantic priors provided by large vision-language models (VLMs). However, standard action supervision does not explicitly encourage intermediate representations to preserve scene geometry, relative orientation, or global episode progress. Incorporating depth estimators, explicit maps, point clouds, or geometry foundation models at inference can provide such structure but introduces additional computation, memory overhead, and architectural dependence during deployment. We introduce StageVLN, a training framework that shapes navigation representations through privileged spatial and trajectory guidance while preserving the original inference pathway. A frozen geometry foundation model provides multi-level spatial guidance to hierarchical navigator states, while relative-heading and expert-route progress objectives provide complementary trajectory-state supervision. All auxiliary components are used only during training and removed at deployment. On R2R-CE validation-unseen, StageVLN achieves 56.3% SR and 51.4% SPL with a 4B-parameter backbone, without an additional geometry encoder at inference. On RxR-CE, it achieves 54.3% SR without additional navigation training data or a geometry encoder at inference.

Anh Dao, Quan-Dung Pham, Le Danh Vinh · 243 views · Oct 5, 2026

Vision-language navigationEmbodied AI

Oct 5, 2026

How Machines Sense the World: A Practical ESP32 Sensor Guide

From a dusk-light LDR circuit to a multi-sensor rover, one ESP32-S3 guide connects GPIO, ADC, I2C, SPI, hysteresis, persistence, and fail-safe sensing into a testable perception loop.

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ESP32Embedded Systems

ROS 2 3D LiDAR Localization on Prior Maps

@rsasaki0109 · 176 views · 2026-10-16

ROS2LiDAR Localization