Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.06010

Recon2Servo: Robotic Ultrasound Visual Servoing via Learned Image-to-Motion Inference

Yameng Zhang·Pei Liu·Dianye Huang·Yizhao Qian·Zhongyu Chen·Xiangyu Chu·K. W. Samuel Au·Zhongliang Jiang
View original source ↗Read source document ↗Export metadata ↓

Abstract

Ultrasound visual servoing is essential for autonomous robotic ultrasound, yet 6-DoF probe control from 2D B-mode images remains challenging due to limited and ambiguous out-of-plane motion cues. Existing methods typically rely on anatomical priors or handcrafted visual features, limiting their generalizability across imaging targets. Inspired by trackerless 3D ultrasound reconstruction, we propose Recon2Servo, a visual servoing framework that learns image-to-motion inference directly from B-mode images for 6-DoF probe control. A DINOv3 encoder with low-rank adaptation and a bidirectional relation module estimate the relative probe pose between current and target images to guide iterative closed-loop target-view alignment. The framework combines supervised relative-pose learning, reconstruction-guided closed-loop adaptation, and bounded residual pose correction to improve motion inference during servoing. Evaluations on a public dataset and an in-house dataset collected from 12 healthy volunteers using different ultrasound systems demonstrate its effectiveness in reconstructed-volume servoing. Additional real-robot demonstrations of target-view alignment and dynamic tracking on a human forearm are provided in the supplementary video: https://youtu.be/qwsOdI-GMYk.

Explore related subjects

cs.RO

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yameng Zhang, Pei Liu, Dianye Huang, Yizhao Qian, Zhongyu Chen, Xiangyu Chu, K. W. Samuel Au, Zhongliang Jiang. 2026-10-05. Recon2Servo: Robotic Ultrasound Visual Servoing via Learned Image-to-Motion Inference. https://arxiv.org/abs/2610.06010

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

KEEP EXPLORING

Related papers

arXiv2026

Disambiguate Gripper State in Grasp-Based Tasks: Pseudo-Tactile as Feedback Enables Pure Simulation Learning

Grasp-based manipulation tasks are fundamental to robots interacting with their environments, yet gripper state ambiguity significantly reduces the robustness of imitation learning policies for these tasks. Data-driven solutions face the challenge of high real-world data costs, while simulation data, despite its low costs, is limited by the sim-to-real gap. We identify the root cause of gripper state ambiguity as the lack of tactile feedback. To address this, we propose a novel approach employing pseudo-tactile as feedback, inspired by the idea of using a force-controlled gripper as a tactile sensor. This method enhances policy robustness without additional data collection and hardware involvement, while providing a noise-free binary gripper state observation for the policy and thus facilitating pure simulation learning to unleash the power of simulation. Experimental results across three real-world grasp-based tasks demonstrate the necessity, effectiveness, and efficiency of our approach.

cs.RO↗
arXiv2026

Video Replanning via Latent Embedding Refinement and Rejection-Based Sampling

Video planning has emerged as a flexible framework for robot manipulation, in which a generative model predicts a video of task completion, and a downstream module translates the predicted frames into actions. However, existing methods typically ignore information from past interactions, limiting their ability to adapt to latent physical properties that can only be revealed through trial and error, such as whether a door should be pushed or pulled, or how friction affects object dynamics. When a plan fails, these methods usually replan from scratch without leveraging the information revealed by the failure. To address this limitation, we introduce RELIC, REplanning with Latent embedding refInement and Candidate rejection, a video planning framework that adapts to hidden physical properties from test-time failures. RELIC optimizes a latent embedding that captures the environment's hidden physical properties from interaction videos and introduces a rejection-based sampling mechanism that filters out hypotheses inconsistent with prior failures. Across eight tasks in two simulation suites, RELIC consistently reduces the number of replanning steps required for success, and linear probes show that its embedding captures the hidden parameters from the interaction itself rather than from scene appearance. Across four challenging real-world robotic manipulation tasks involving hidden interaction modes, e.g., friction, center of mass, and object mass, RELIC raises the one-shot replanning success rate of a video planning baseline from 30.0% to 63.8% after a single physical interaction.

cs.RO↗
arXiv2026

Least Restrictive Hyperplane Control Barrier Functions

Control Barrier Functions (CBFs) can provide provable safety guarantees for dynamic systems. However, finding a valid CBF for a system of interest is often non-trivial, especially for systems with low computational resources, higher-order dynamics, and moving close to obstacles of complex shape. A common solution to this problem is to use a purely distance-based CBF. In this paper, we study Hyperplane CBFs (H-CBFs), where a hyperplane separates the agent from the obstacle. First, we note that the common distance-based CBF is a special case of an H-CBF where the hyperplane is a supporting hyperplane of the obstacle that is orthogonal to a line between the agent and the closest point of the obstacle. We then show that a less conservative CBF can be found by optimising over the orientation of the supporting hyperplane, in order to find the Least Restrictive Hyperplane CBF. This enables us to maintain the safety guarantees while allowing controls that are closer to the desired ones, especially when moving fast and passing close to obstacles. We illustrate the approach on a double integrator dynamical system with acceleration constraints, moving through a group of arbitrarily shaped static and moving obstacles, and show that the proposed approach reduces the average gap between desired and safe controls by an order of magnitude.

cs.RO↗