A shared-autonomy framework that assists the operator throughout this process ofTeleoperating a robotic manipulator in industrial environments demands precision that camera-based interfaces alone struggle to deliver and is validated on a quadruped mobile manipulator.
Abstract
Teleoperating a robotic manipulator in industrial environments demands precision that camera-based interfaces alone struggle to deliver. The operator must align the end-effector with a target in clutter, under limited depth perception, and without colliding with the surrounding structures. This paper presents a shared-autonomy framework that assists the operator throughout this process. A single RGB-D camera captures the operator's arm motion and hand gestures without wearables, fiducials, or a calibration stage. The intended target is specified by a free-form text prompt, grounded by a vision-language model in the robot's gripper camera, and tracked across its onboard cameras by a promptable video-segmentation model, resulting in a grasp frame continuously separated from the obstacle map. Every commanded motion is executed by a GPU-accelerated model-predictive controller that enforces self- and environment-collision avoidance against an online volumetric reconstruction, while a potential field corrects the operator's reference toward the grounded target during the final approach. An autonomous mode can be gesture-triggered to complete the grasp on the same target without a separate perception pipeline. The framework is validated on a quadruped mobile manipulator. The interface achieves a positional RMSE of 59 mm relative to motion-capture ground truth, and the controller keeps the arm at least 18 cm from obstacles while the operator deliberately commands the arm into them by 6 cm. In an industrial valve manipulation and a pick-and-place task, the full framework succeeded in all trials, while ablating either the collision or the assistance module produced failures through complementary mechanisms, and autonomous execution succeeded in four of five trials per task.
Open-vocabulary grasping on a quadruped manipulator requires more than recognizing the target object. The robot must also select a grasp pose that is both consistent with the task semantics and reliable to execute under body motion and viewpoint changes. In this paper, we present VLEG, an embodied vision-language grasping framework for quadruped manipulators that explicitly incorporates body motion into grasp decision making. Our method guides the robot to continuously adjust its body pose during approach and optimize local observations before grasping, thereby improving perception quality. For grasp decision making, instead of using a coarse single-stage filtering strategy, we design a multi-stage and multi-criteria grasp selection mechanism based on geometric grasp candidates. This mechanism jointly considers physical feasibility and task consistency. We implement the complete system on an onboard Jetson platform and conduct extensive real-world experiments on a quadruped robot equipped with a manipulator, covering tabletop, low-platform, ground-level, and outdoor raised-platform scenes. The results validate the deployability of VLEG in real-world quadruped manipulation scenarios, as well as its robust grasping ability and task-aware decision-making capability across the tested object categories.
Yu-Xing Ji, Fei Meng, Zishang Ji et al.· Journal of Physics, Conferen...· 0 citations
Teleoperating multiple robots simultaneously enables additional views and coordinated control. Yet, it poses fundamental challenges: the system must present sensor data cohesively and allow operators to manage multiple robot bases, arms, and cameras while maintaining low latency. Current multi-robot teleoperation systems require multiple operators, rely on autonomy, or restrict operators to high-level commands. We present GHOST: an open-source VR teleoperation system that enables single operator control of two mobile manipulators via direct lowlevel commands using only onboard sensing. GHOST creates an exocentric 3D workspace by aligning real-time point clouds from the robots'RGB-D cameras, where scene coverage is improved through learning-based completion to aid operator spatial awareness. For control, the operator uses a mode-switching architecture to command either robot individually or both robots simultaneously. Experiments with 15 novice participants demonstrate 1.6-4x the success rate of an off-the-shelf tablet interface. For experts across nine challenging dual-robot tasks, our system enabled completion of two tasks that were infeasible with the tablet, and was 1.47x faster on average than the tablet. Website and code: https://h2r.github.io/GHOST/.
Yichen Wei, Faisal Zaghloul, Soujanya C Aryal et al.· IEEE Robotics and Automation...· 0 citations
This work added YOLO-based object and hand detection, stereo vision-based localization using the robot's built-in low-resolution fisheye cameras, and task-specific corrections for grasp execution to form a novel calibration-based grasping pipeline that does not require RGB-D cameras, motion capture, or external tracking systems.
Low-cost four-degree-of-freedom (DoF) arms are among the most accessible robotic platforms. But they are, in theory, underactuated for picking up in situations where objects are at arbitrary orientations, a task that appears to require five degrees of freedom: the planar position (x and y), the height (z), a wrist rotation to align the gripper with the object, and gripper actuation, of which a four-DoF arm lacks the wrist rotation. This work shows that perception and motion planning can enable such an arm, a roughly $200 Waveshare RoArm-M2-S, under a fixed overhead camera to detect and color-sort writing utensils without that joint. A YOLO11n-OBB (You Only Look Once, oriented bounding box) detector locates each writing utensil; camera intrinsics and an ArUco reference pose convert its pixel coordinates to robot coordinates; and a color classifier labels it. The detected orientation angle determines the motion strategy: utensils close to the arm's fixed approach direction are picked up directly, and those at steeper angles are reoriented via corrective sweeps until they are graspable, after which they are picked up and sorted into the assigned color bin. Across 326 logged motions on seven writing utensils, the arm made 196 direct grasps and 130 corrective sweep passes, correcting misalignments up to 90 degrees, suggesting that clever task-informed engineering can compensate for a missing degree of freedom on tasks like this one.
Robot manipulation is a complex task that requires visual understanding, physical reasoning, planning, and closed-loop control. General-purpose foundation models (FMs) have grown remarkably capable of some of these, especially vision and reasoning. To leverage this for generalist robot policies, current methods typically involve converting existing FMs into vision-language-action (VLA) models by fine-tuning on robot data to output low-level actions. However, VLAs are often orders of magnitude smaller than frontier FMs given the limited data and compute available for fine-tuning, which in turn limits their general capability. Inspired by the growing ability of FMs to operate software through visual interfaces, we ask whether that same competence suffices to control a robot. We present VIA (Visual Interface Agent for robot control), a framework that recasts robot control as an agentic task: an off-the-shelf FM-powered agent drives a manipulator through a browser-based 3D interface by taking screenshots, issuing intuitive commands, observing the outcome, and adjusting. The agent receives no robot-specific fine-tuning and no access to privileged state information: it perceives visual input and acts through a small set of general tools. VIA inherits the agent's general reasoning, closed-loop error recovery, and ability to plan and re-plan from what it observes. It solves a diverse suite of tabletop manipulation tasks zero-shot with both Claude Code and Codex. With the strongest model (Fable 5) it achieves 96.7% success on three LIBERO-Goal tasks and 100% on a long-horizon rainbow assembly task. Performance improves with the scale and strength of the underlying model. These results suggest that frontier agents already possess skills that transfer directly to robot control given the right interface: your coding or computer-use agent is, in a sense, secretly a robot-control agent.
A deep examination of the vision-based object manipulation through collaborative robotics with respect to perception pipelines, object detection and recognition, pose estimation, grasp planning, and real time control integration is given.
Rahul Mehta· International Journal of Int...· 0 citations
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