Searcharxiv⌕ Search

arXiv · 2609.27905

Anchor-Free Hidden-Target Seeking via Certified Self-Calibration under Correlated Odometry

Abstract

We study hidden-target seeking in an anchor-free regime where neither the vehicle, the relay, nor the target has an accessible global pose. The vehicle never senses the target directly; it receives only range-bearing observations through a single relay of unknown position and orientation, with motion known only via integrated body-frame odometry. Absolute localization is fundamentally impossible: the joint configuration retains an exact three-dimensional SE(2) gauge no estimator can resolve. Yet the quantities needed for control remain fully recoverable: motion collapses calibration to a task-relevant quotient, the relay-to-odometry yaw and target displacement, identifiable in closed form from two distinct vehicle views. We introduce an O(K) multi-view self-calibration estimator with an exact first-order yaw-uncertainty certificate that propagates the cross-view correlations integrated odometry induces: the full certificate attains 95.0% pooled coverage at the nominal 95% level, versus 82.1% when correlated poses are treated as independent. The certificate drives a hybrid policy that excites until calibration is trustworthy, refuses uncertified estimates, seeks using continuously re-measured geometry, and detects relay-frame changes via persistent certified inconsistency, avoiding unbounded dead-reckoning drift. Across 200 randomized closed-loop trials, the method attains 0.064 m median station error versus 0.065 m for an oracle given the true relay yaw, despite 27 m median dead-reckoning drift over long horizons; 90 physics-based ROS 2/Gazebo trials retain 30/30 success under nominal operation, communication degradation, and relay-frame disturbances. These results show global localization is unnecessary for reliable hidden-target seeking under this single-relay model, even when both the sensing infrastructure and the vehicle's own reference frame are uncalibrated.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yash Bagla. 2026-08-26. Anchor-Free Hidden-Target Seeking via Certified Self-Calibration under Correlated Odometry. https://arxiv.org/abs/2609.27905

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Physics-Guided Residual Reinforcement Learning for Humanoid Narrow-Path Traversal

Traversing narrow paths is challenging for humanoid robots due to the sparse and safety-critical footholds required. Purely template-based or end-to-end reinforcement learning-based methods suffer from such harsh terrains. This paper proposes a two stage training framework for such narrow path traversing tasks, coupling a template-based foothold planner with a low-level foothold tracker from Stage-I training and a lightweight perception aided foothold modifier from Stage-II training. With the curriculum setup from flat ground to narrow paths across stages, the resulted controller in turn learns to robustly track and safely modify foothold targets to ensure precise foot placement over narrow paths. This framework preserves the interpretability from the physics-based template and takes advantage of the generalization capability from reinforcement learning, resulting in easy sim-to-real transfer. The learned policies outperform purely template-based or reinforcement learning-based baselines in terms of success rate, centerline adherence and safety margins. Validation on a Unitree G1 humanoid robot yields successful traversal of a 0.2m wide and 3m long beam for 20 trials without any failure.

cs.RO↗

HERMES: A Holistic End-to-End Risk-Aware Multimodal Embodied System with Vision-Language Models for Long-Tail Autonomous Driving

End-to-end autonomous driving models increasingly benefit from large vision-language models for semantic understanding, yet safe and reliable planning under long-tail conditions remains challenging, particularly in mixed-traffic environments involving heterogeneous road users and rare safety-critical interactions. This paper proposes HERMES, a holistic risk-aware end-to-end multimodal driving framework that explicitly incorporates long-tail semantic knowledge into trajectory planning. HERMES employs a foundation-model-assisted annotation pipeline to construct structured Long-Tail Scene Context and Long-Tail Planning Context, capturing hazard-centric scene information, maneuver intent, and risk-aware planning guidance. A Tri-Modal Driving Module then integrates multi-view visual observations, historical ego-motion, and long-tail semantic instructions through intent- and risk-aware conditioning for trajectory generation. Extensive experiments on a large-scale real-world long-tail driving benchmark demonstrate consistent improvements over representative recent baselines in overall planning performance and across diverse safety-critical scenarios. Ablation studies further validate the effectiveness and complementary roles of the major components within HERMES.

cs.RO↗

StageCraft: Execution Aware Mitigation of Distractor and Obstruction Failures in VLA Models

Large scale pre-training on text and image data along with diverse robot demonstrations has helped Vision Language Action models (VLAs) to generalize to novel tasks, objects and scenes. However, these models are still susceptible to failure in the presence of execution-time impediments such as distractors and physical obstructions in the robot's workspace. Existing policy improvement methods finetune base VLAs to improve generalization, yet they still struggle in unseen distractor settings. To address this problem, we investigate whether internet-scale pretraining of large vision-language models (VLMs) can be leveraged to reason about these impediments and mitigate policy failures. To this end, we propose StageCraft, a training-free approach to improve pretrained VLA policy performance by manipulating the environment's initial state using VLM-based in-context reasoning. StageCraft takes policy rollout videos and success labels as input and leverages VLM's reasoning ability to infer which objects in the initial state need to be manipulated to avoid anticipated execution failures. StageCraft is an extensible plug-and-play module that does not introduce additional constraints on the underlying policy, and only requires a few policy rollouts to work. We evaluate performance of state-of-the-art VLA models with StageCraft and show an absolute 40% performance improvement across three real world task domains involving diverse distractors and obstructions. Our simulation experiments in RLBench empirically show that StageCraft tailors its extent of intervention based on the strength of the underlying policy and improves its performance with more in-context samples. Videos of StageCraft in effect can be found at https://stagecraft-decorator.github.io/stagecraft/ .

cs.RO↗