Accurate needle steering using bevel-tip needles remains challenging due to nonlinear needle-tissue interactions and structural limitations of conventional robotic insertion systems in imaging-guided environments. This paper presents a cable-driven parallel robot (CDPR)-based needle steering framework that enables curvature-induced steering through coordinated control of the needle base pose. The proposed system provides 6-DoF needle orientation control using eight cables and an additional Bowden cable mechanism for axial rotation. Phantom insertion experiments demonstrate that steering direction can be regulated by bevel-tip orientation and that obstacle-avoidance insertion toward a desired target location is achievable. These results confirm the feasibility of CDPR-based needle steering for imaging-compatible minimally invasive intervention scenarios.
Autonomous Mobile Robots are typically limited to structured environments, as conventional wheeled propulsion often fails on deformable terrains like sand due to excessive wheel slip and sinkage. To address this mobility challenge, this paper introduces a novel locomotion strategy for a high-degree-of-freedom wheel-legged robot. The proposed method is a gait based on an asymmetric "dynamic compact-and-push" cycle, where the robot's limbs perform a paddling-like motion to actively remodel the granular media. This active terrain remodeling allows the robot to generate net forward thrust where conventional wheeled locomotion is ineffective. We systematically designed and experimentally validated four distinct gaits founded on this principle. The results demonstrate that this approach enables sustained forward motion in an environment where wheeled propulsion is verified to fail, with the asynchronous paddling gait proving most effective. This work contributes a new, validated locomotion mechanism for sand terrains and provides a quantitative comparison of different limb coordination strategies.
Containerization and orchestration using cloud-native technologies enable scalable deployment of robotic software. Integrating ROS~2 with Kubernetes offers a flexible infrastructure, but also introduces a complex, multi-layered communication stack - from DDS middleware to container networks and the physical layer. Each layer adds overhead and variability that impact application-level performance. This paper presents a comprehensive analysis of communication performance across the cloudedgerobot continuum, focusing on throughput and one-way latency in scalable ROS~2 deployments. We evaluate communication across intra-robot, edge, and cloud segments using wired and wireless connections, including emerging technologies like Wi-Fi~7 and high-speed LAN. Using a Kubernetes-based testbed, we investigate various ROS~2 middlewares, CNI plugins, QoS configurations, and encryption options. Our experiments reveal the impact of network overlays, routing paths, and middleware choices on latency and bandwidth. Despite the inherent complexity, the results confirm the feasibility of deploying ROS~2 in orchestrated, scalable environments. We summarize key insights as practical takeaways, many of which apply beyond Kubernetes, to guide the design of robust cloud/edge robotic systems.
Large-area tactile sensing remains a key challenge for wearable and robotic applications, where solutions must balance resolution and complexity, manufacturability, and conformability to various geometries. While acoustic waveguides have been used for contact localization and force estimation at the centimeter scale, scaling this technology to limb-scale wearable devices is unexplored. In this work, we introduce a soft, wearable tactile sleeve based on wrapped and meter-length acoustic waveguides. By patterning waveguides on a sleeve, one-dimensional time-of-flight measurements are mapped to two-dimensional contact locations. This enables conformable coverage with sparse transducers, while preserving mechanical robustness by placing rigid electronics away from the contact surface. We contribute the design and fabrication of the waveguide-based tactile sensor, provide an in-depth characterization of sensor response and evaluate frameworks for contact localization and force estimation, and demonstrate system performance on a human arm. Results show that the time-of-flight-based localization approach generalizes across contact sizes and curved geometries. However, more work is required to achieve sensitive and reliable force estimates. This work establishes acoustic waveguides as a manufacturable and reconfigurable modality for wearable tactile skins.
This paper introduces Mapping-based Tasks for Inspection: Discovery and Allocation (Map-TIDAL), a method for generating environmentally informed tasks and distributing them in a heterogeneous multi-robot system for visual inspection of underwater structures. Map-TIDAL leverages the individual robot maps generated during SLAM (without prior knowledge of the environment) and tasks from all the robots through a communication-aware auction process to determine additional inspection locations as the structures are further explored by the robots. This allows the method to adaptively focus on geometrically interesting areas that need detailed inspection while still maintaining good overall coverage with a reasonably small number of inspection tasks. Experiments on both saline and fresh water tanks show that Map-TIDAL yields better coverage while inspecting areas with interesting geometric features more thoroughly, using equal or fewer inspection locations compared to prevalent coverage methods using Voronoi distributions and boustrophedon patterns.
Conventional motor-driven wearable robots often suffer from increased weight and limited torque output. To address this issue, this study proposes a motorSMA hybrid actuation approach that combines the advantages of electric motors and shape memory alloy (SMA) actuators. A dedicated testbed was developed to evaluate the proposed method under varying load conditions. Experimental results show that the SMA actuator provides additional assistive torque of approximately 3 N·m compared to motor-only operation, without significant increase in system weight. These results demonstrate the feasibility of hybrid actuation for achieving lightweight and high-performance wearable robotic systems.
Learning-based controllers are increasingly adopted in lower-extremity powered exoskeletons, yet their advantages over traditional adaptive approaches remain underexplored. We compared two adaptive assist-as-needed (AAN) controllers for gait training with an ankle exoskeleton: a reinforcement learning-based controller (RL-AAN) and a conventional iterative learning controller (ILC-AAN). Both adjusted assistance stride-by-stride, delivering torque as a percentage of the wearer's biological plantarflexion momentestimated online with a subject-agnostic modeland progressively faded assistance as performance improved. Healthy participants walked on a self-paced treadmill under a perturbed-gait protocol. Performance was assessed as average percent stride-velocity (SV) improvement relative to unassisted perturbed walking (Δ%εSV+) and percent of strides above the SV threshold (N%SV+). During training, RL-AAN and ILC-AAN elicited comparable gains in Δ%εSV+ between the first and last training sessions, but RL-AAN yielded greater adherence across sessions, as indicated by larger N%SV+. After training, RL-AAN demonstrated superior retention in Δ%εSV+ and N%SV+. These results support RL-AAN as a promising strategy for subject-tailored gait training, motivating future studies in neurological and musculoskeletal populations.
Manipulators are essential for advancing orchard robotics tasks such as pruning and harvesting, which require precise, dexterous motion in cluttered and unstructured environments. Off-the-shelf industrial arms, while readily available, often lack the reach and dexterity required for these settings. In this paper we present a simulation-driven, multi-objective optimization framework for task-specific manipulator kinematics, leveraging the NSGA-II evolutionary algorithm and physics-based evaluation. Candidate designs are encoded with high-level parameters -- joint type, axis orientation, link length, and joint count -- then automatically generated as URDF models and evaluated in simulation for reachability, manipulability, torque demand, and motion planning cost. Trade-offs are revealed on a Pareto front, enabling exploration across diverse designs. The framework is demonstrated on a real-world tree pruning task, using collected 3D scans of expert-pruned trees and an automated prune point identification pipeline to generate target points to guide the optimization. Results show that the proposed approach produces task-specific manipulator designs with improved workspaces and reduced operational constraints compared to a commercial industrial arm, offering a viable pathway toward deployable agricultural manipulation systems.
Suturing is a high-precision task performed at the end of procedures when surgeon fatigue may increase errors, highlighting the need for robot assistance. Previous autonomous suturing works, such as STITCH 1.0 [1], struggle to fully close wounds due to inaccurate needle tracking, thread tangling, and poor insertion placement. To address these challenges, we present STITCH 2.0, an improved augmented dexterity pipeline over STITCH 1.0 using the da Vinci Research Kit (dVRK) [2] with seven improvements including an improved EKF needle pose estimation pipeline, new thread untangling methods, and an automated 3D suture alignment algorithm. Experimental results over 15 trials (maximum 450 individual suture trials) find that STITCH 2.0 achieves 74.4% wound closure with an average of 4.87 sutures per trial, representing 66% more completed sutures in 38% less time compared to STITCH 1.0 [1]. When two human interventions are allowed, STITCH 2.0 averages six sutures with a 100% wound closure rate.
Soft continuum robots are gaining attention for their potential to enable inherently safe and adaptive human-robot collaboration, especially in dynamic industrial environments. However, the development of these robots varies drastically and no standardization exists. This is particularly problematic for soft continuum robots, because of the variety of different actuation methods, and control strategies. This paper addresses the challenge of engineering soft continuum robots by introducing a generalized framework that enables hardware abstraction and controller reuse. The approach combines a modular robot design by extending the Unified Robot Description Format (URDF) information model to support soft continuum robotics. Enabling the decoupling of hard- and software development. For the concept validation, a modular tendon-driven continuum robot was developed and integrated into the framework. The extended Unified (Continuum) Robot Description Format (U(C)RDF) enables visualization and controller parameterization through standardized interfaces, allowing for reusable software components across different actuation principles. This approach achieves a flexible and scalable engineering process for soft continuum robots, bridging the gap between research prototypes and industrial deployment. It lays the foundation for future developments in model-based design, automated control, and interoperability of soft continuum robotic systems.
This study addresses the challenge of estimating the three-dimensional(3D) position of a needle tip from two-dimensional(2D) X-ray images. We propose a classical image processingbased framework for needle tip localization and 3D reconstruction. The method first detects a circular marker attached to the robotic end-effector that controls the needle insertion and identifies the needle head position within the marker. Preprocessing steps, including bilateral filtering, thresholding, and iterative morphological operations, are applied to improve image quality and ensure the continuity of the needle shaft. A flood-fill algorithm is then used to segment the needle body, after which the needle trajectory is extracted using the A^star algorithm. Finally, the 3D position of the needle tip is reconstructed by Triangulation from multiple X-ray images acquired at different viewing angles.
Depth completion from sparse LiDAR points and images is a key perception task for autonomous robots, enabling dense 3D understanding in challenging environments. However, most recent researches achieve accuracy gains by greatly enlarging network size, making them unsuitable for realtime deployment on power- and compute-constrained platforms. This paper proposes an ultra-lightweight depth completion framework optimized for embedded systems. Our approach integrates a re-parameterized encoderdecoder with fewer than 5M parameters and a two-stage hybrid distillation strategy. The first stage progressively densifies sparse depth supervision, while the second preserves edge fidelity through a combination of metric and structural losses. A full TensorRT FP16 pipeline further ensures efficient deployment. Extensive experiments on KITTI Depth Completion, NYU-v2 . demonstrate that our method achieves competitive accuracy while maintaining high efficiency. On a Jetson Xavier NX, the system runs at over 30 FPS with sub-33 ms latency within a 20 W power envelope, showing strong potential for real-world micro-robotic platforms. We will open-source the code to benefit the community. Our open source website: https://github.com/2463450186Q/JetsonCompletion.git
We present a scalable framework for cross-embodiment humanoid robot control by learning a shared latent representation that unifies motion across humans and diverse humanoid platforms, including single-arm, dual-arm, and legged humanoid robots. Our method proceeds in two stages: first, we construct a decoupled latent space that captures localized motion patterns across different body parts using contrastive learning, enabling accurate and flexible motion retargeting even across robots with diverse morphologies. To enhance alignment between embodiments, we introduce tailored similarity metrics that combine joint rotation and end-effector positioning for critical segments, such as arms. Then, we train a goal-conditioned control policy directly within this latent space using only human data. Leveraging a conditional variational autoencoder, our policy learns to predict latent space displacements guided by intended goal directions. We show that the trained policy can be directly deployed on multiple robots without any adaptation. Furthermore, our method supports the efficient addition of new robots to the latent space by learning only a lightweight, robot-specific embedding layer. The learned latent policies can also be directly applied to the new robots. Experimental results demonstrate that our approach enables robust, scalable, and embodiment-agnostic robot control across a wide range of humanoid platforms.
This paper presents an innovative and practical method for robotic needle steering in radio-frequency ablation (RFA) to treat cancer. One of the main challenges in this process is that tissue shifts and deforms during needle insertion, making it difficult to accurately predict the needle's path in real time. Inverse finite element (iFE) simulations have been used to address this problem. While these methods are accurate, they often require further refinement for effective time performance in real-world robotic systems. This is because when the method is incorporated into a real robot, there can be a delay in command execution. To address this challenge, we propose a machine learning-based solution that learns from offline simulations, shifting the intensive calculations required by iFE methods to an offline training stage and enabling online prediction of tissue deformation with reduced computational time. Our network was trained on data from numerous simulated needle insertions to capture interactions among insertion forces, tissue properties, and resulting motion. Once trained, the model produces predictions almost instantaneously, making it suitable for real-time applications. We validated the approach by steering the needle in a simulated deformable, moving gel to compare it with numerical-based methods, and then performing needle steering within a reconstructed human body that involves multiple structures and integrates the robot's dynamics. The results demonstrated that the developed networks achieved slightly better accuracy in the first scenario while also running faster, resulting in improved performance under the robot's dynamics. These findings show that our method is a promising advancement toward real-time guidance systems for needle-based medical procedures.
We propose a visual-servoing and obstacle-avoidance controller for a wheeled mobile robot (WMR) with a two-axis gimbal camera that operates without mapping, using only vision and lightweight forward sensing. A task-allocation MPC with online terminal-cost iteration is introduced. Specifically, task projection in the image-feature space mitigates underactuation and couplinginduced local optima; Virtual Imaging Constraint Guidance (VICG) yields a visibility-preserving heading reference that steers the trajectory around obstacles; and an Approximate Dynamic Programming (ADP) module learns a context-aware terminal cost online, providing long-horizon guidance for mid-horizon prediction. Relying solely on image feedback plus lightweight ranging, the method coordinates the WMR and gimbal to accomplish obstacle avoidance and visual-servo tracking jointly. Hardware experiments validate the feasibility and effectiveness of the proposed approach.
This work introduces a novel compliant model for running gaits. The model consists of a linear leg stiffness paired with a nonlinear energy regulation term. This new model, termed the quartic model, is shown to reproduce the external dynamics of a running gait. The characteristics of the gait are imposed through parametric conditions which are derived through linearization of the model. The nonlinear nature of the model ensures convergence towards a limit cycle, which makes the model a useful template for the control of legged systems.
Recovering upper-limb motor functions impaired by trauma or neurological disease is a long and challenging process. To monitor a patients progress through the various stages of rehabilitation and guide therapy, regular movement assessment is essential. However, such evaluations are rarely conducted in clinical practice due to time constraints and the need for cumbersome equipment. A key limitation is the access to reference motion data, typically derived from averaged movements of unimpaired individuals, which requires new data collection for each task and lacks personalization (e.g., accounting for individual morphology or motor abilities). We present a novel method to generate patient-specific reference motions directly from the patients hand pose using a personalized model of the patient, the Virtual Humanoid Twin (VHT). By solving an ergonomic-based optimal control problem, our approach produces tailored reference motions without prior task-specific data. We validated this method on two motor tasks (reaching and pouring) using data from seven unimpaired participants, with and without an elbow orthosis restricting motion. Analysis of joint trajectories, range of motion, and normalized multi-dimensional Dynamic Time Warping confirmed that VHT-generated motions were more ergonomic than those with the orthosis and closely matched natural movements. The methods rapid generation time can also enable real-time reference motion estimation, parallel to the patients movements. This innovation simplifies access to reference motions while providing personalization. It creates opportunities for automated motor assessment in neurorehabilitation, enhancing patient recovery tracking through regular evaluations.
