arXiv AI

PiDR: Physics-Informed Inertial Dead Reckoning for Autonomous Platforms

arXiv:2601. 03040v2 Announce Type: replace-cross Abstract: A fundamental requirement for full autonomy is the ability to sustain accurate navigation in the absence of external data, such as GNSS signals or visual information.

arXiv AI

Jul 8

Physics-Regularized Machine Learning for Proprioceptive Vehicle Localization Using Onboard Sensors

arXiv:2607. 05663v1 Announce Type: cross Abstract: Accurate and robust localization is essential for autonomous mobility systems in real-world environments.

By Abinav Kalyanasundaram, Karthikeyan Chandra Sekaran, Wolfgang Utschick, Michael Botsch

agents

arXiv Machine Learning

Jul 8

Uncertainty-Aware Velocity Correction for Proprioceptive Vehicle Localization using Evidential Mamba

arXiv:2607. 05669v1 Announce Type: cross Abstract: Reliable localization in GNSS-denied environments remains a fundamental challenge for intelligent vehicles, as inertial navigation systems accumulate unbounded drift without external correction.

By Abinav Kalyanasundaram, Karthikeyan Chandra Sekaran, Wolfgang Utschick, Michael Botsch

arXiv AI

Jul 22

Physical Self-Supervised Learning: IMU Sensing without Manual Labels

arXiv:2607. 18361v1 Announce Type: cross Abstract: Deep neural networks have become a promising approach for IMU-based sensing, but their scalability is fundamentally limited by costly labeled data and poor robustness to heterogeneous devices, placements, and users.

By Yuyang Leng (Richard), Renyuan Liu (Richard), Shaohan Hu (Richard), Peijun Zhao (Richard), Chun-Fu Chen (Richard), Songqing Chen, Shuochao Yao

diffusion fine-tuning benchmarks

arXiv Machine Learning

Jun 25

Tracking Large-scale Shared Bikes with Inertial Motion Learning in GNSS Blocked Environments

arXiv:2605. 07412v2 Announce Type: replace Abstract: Although Global Navigation Satellite Systems (GNSS) provide a general solution for bike tracking outdoors, there still exist complex riding environments where only inertial navigation systems work, such as urban canyons.

By Feng Liu (Beijing Jiaotong University), Kejia Li (Beijing Jiaotong University), Zhiwei Yang (DiDi Company), Chunwei Yang (DiDi Company), Qun Li (DiDi Company), Guobin Wu (DiDi Company), Qiang Ni (Lancaster University), Ruipeng Gao (Beijing Jiaotong University)

arXiv AI

Aug 7

TRACE: Learned Proprioceptive Odometry for Legged Robots under Unreliable Contact Conditions

arXiv:2608. 05975v1 Announce Type: cross Abstract: In this paper, we present TRACE (Tokenized Robust Attention for Contact-Aware Estimation), an end-to-end learned proprioceptive odometry estimator for legged robots under unreliable contact conditions.

By Taehyeon Kong, Woojin Kim, Jemin Hwangbo

robotics fine-tuning benchmarks

arXiv Machine Learning

Aug 3

ASVSim (AirSim for Surface Vehicles): A High-Fidelity Simulation Framework for Autonomous Surface Vehicle Research

arXiv:2506. 22174v3 Announce Type: replace-cross Abstract: The transport industry has recently shown significant interest in unmanned surface vehicles (USVs), specifically for port and inland waterway transport.

By Bavo Lesy, Siemen Herremans, Robin Kerstens, Jan Steckel, Walter Daems, Siegfried Mercelis, Ali Anwar

agents computer-vision reinforcement-learning safety

arXiv Machine Learning

Jul 15

APPLV: Adaptive Planner Parameter Learning from Vision-Language-Action Model

arXiv:2603. 08862v2 Announce Type: replace-cross Abstract: Autonomous navigation in highly constrained environments remains challenging for mobile robots.

By Yuanjie Lu, Beichen Wang, Zhengqi Wu, Yang Li, Xiaomin Lin, Chengzhi Mao, Xuesu Xiao

llms agents reinforcement-learning robotics fine-tuning efficiency multimodal benchmarks safety

Hugging Face Trending Papers

Aug 6

TRACE: Learned Proprioceptive Odometry for Legged Robots under Unreliable Contact Conditions

In this paper, we present TRACE (Tokenized Robust Attention for Contact-Aware Estimation), an end-to-end learned proprioceptive odometry estimator for legged robots under unreliable contact conditions. The proposed estimator directly predicts relative displacement, relative rotation, and body-frame velocity from a recent history of onboard inertial and joint measurements.

robotics fine-tuning benchmarks

arXiv Machine Learning

Jun 24

SkyJEPA: Learning Long-Horizon World Models for Zero-Shot Sim-to-Real Control of Quadrotors

arXiv:2606. 23444v2 Announce Type: replace-cross Abstract: Accurate dynamics models are critical for informed decision-making in robotic systems, particularly for agile aerial vehicles operating under uncertainty.

By Pratyaksh Rao, Wancong Zhang, Randall Balestriero, Yann LeCun, Giuseppe Loianno

rag diffusion robotics

arXiv AI

Jul 14

RVN-Bench: A Benchmark for Reactive Visual Navigation

arXiv:2603. 03953v2 Announce Type: replace-cross Abstract: Safe visual navigation is critical for indoor mobile robots operating in cluttered environments.

By Jaewon Lee, Jaeseok Heo, Gunmin Lee, Howoong Jun, Jeongwoo Oh, Songhwai Oh

agents reinforcement-learning robotics benchmarks

arXiv AI

Jun 18

EffiNav: Fusing Depth and Vision-Language for Efficient Object Goal Navigation

arXiv:2606. 18634v1 Announce Type: cross Abstract: To locate a target object while exploring the unknown environment is a fundamental capability for autonomous agents, with applications ranging from search-and-rescue to field robots.

By Zecheng Yin, Benedict Jun Ma

agents robotics multimodal benchmarks

arXiv Machine Learning

Jul 13

Data-Efficient Deep Learning: Empirical Guidelines for Training Set Size Estimation in Inertial Sensor Classification

arXiv:2607. 09402v1 Announce Type: new Abstract: Deep learning models dependency on large-scale inertial datasets presents a significant bottleneck in inertial sensor-based classification tasks, such as human activity recognition and smartphone location recognition.

By Ofir Kruzel, Itzik Klien

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