A Precise Real-Time Force-Aware Grasping System for Robust Aerial Manipulation
- URL: http://arxiv.org/abs/2602.08599v1
- Date: Mon, 09 Feb 2026 12:40:34 GMT
- Title: A Precise Real-Time Force-Aware Grasping System for Robust Aerial Manipulation
- Abstract summary: Aerial manipulation requires force-aware capabilities to enable safe and effective grasping and physical interaction.<n>We propose a novel force-aware grasping framework incorporating six low-cost, sensitive skin-like tactile sensors.<n>The proposed framework enables precise force-aware grasping control, allowing safe manipulation of fragile objects and real-time weight measurement of grasped items.
- Score: 5.804333840515478
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Aerial manipulation requires force-aware capabilities to enable safe and effective grasping and physical interaction. Previous works often rely on heavy, expensive force sensors unsuitable for typical quadrotor platforms, or perform grasping without force feedback, risking damage to fragile objects. To address these limitations, we propose a novel force-aware grasping framework incorporating six low-cost, sensitive skin-like tactile sensors. We introduce a magnetic-based tactile sensing module that provides high-precision three-dimensional force measurements. We eliminate geomagnetic interference through a reference Hall sensor and simplify the calibration process compared to previous work. The proposed framework enables precise force-aware grasping control, allowing safe manipulation of fragile objects and real-time weight measurement of grasped items. The system is validated through comprehensive real-world experiments, including balloon grasping, dynamic load variation tests, and ablation studies, demonstrating its effectiveness in various aerial manipulation scenarios. Our approach achieves fully onboard operation without external motion capture systems, significantly enhancing the practicality of force-sensitive aerial manipulation. The supplementary video is available at: https://www.youtube.com/watch?v=mbcZkrJEf1I.
Related papers
- Real-time Puncture Detection and Recovery for Pneumatic Soft Actuators [2.276860128178926]
We propose a puncture detection system for soft actuators using motion data from a single inertial measurement unit.<n>Anomaly detectors are trained on normal operation data and detect damage through reconstruction errors, while severity is estimated by a separate model trained under slightly modified conditions.<n>This approach enhances the reliability and safety of soft robotic systems through real-time, data-driven damage detection.
arXiv Detail & Related papers (2026-09-08T14:31:59Z) - AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning [56.20593626288444]
AetheRock is an arm-worn device featuring a modular and easily manufactured visuo-tactile sensor, GelSlim-MiniFab, at the fingertip.<n>We propose ForceVT, a representation learning framework that uses force and vision to guide fidelity-agnostic tactile learning.
arXiv Detail & Related papers (2026-06-08T17:38:57Z) - UniForce: A Unified Latent Force Model for Robot Manipulation with Diverse Tactile Sensors [51.88112610411651]
We propose a novel unified tactile representation learning framework that learns a shared latent force space across diverse tactile sensors.<n>UniForce reduces cross-sensor domain shift by jointly modeling inverse dynamics (image-to-force) and forward dynamics (force-to-image)<n>To avoid reliance on expensive external force/torque (F/T) sensors, we exploit static equilibrium and collect force-paired data via direct sensor--object--sensor interactions.
arXiv Detail & Related papers (2026-02-01T11:03:01Z) - Tracing Energy Flow: Learning Tactile-based Grasping Force Control to Prevent Slippage in Dynamic Object Interaction [4.103372358189422]
Regulating grasping force to reduce slippage during dynamic object interaction remains a fundamental challenge in robotic manipulation.<n>We propose a physics-informed energy abstraction that models the object as a virtual energy container.<n> Experiments in both simulation and hardware demonstrate that our method can learn grasping force control from scratch within minutes.
arXiv Detail & Related papers (2025-12-24T08:19:25Z) - PhysHMR: Learning Humanoid Control Policies from Vision for Physically Plausible Human Motion Reconstruction [52.44375492811009]
We present PhysHMR, a unified framework that learns a visual-to-action policy for humanoid control in a physics-based simulator.<n>A key component of our approach is the pixel-as-ray strategy, which lifts 2D keypoints into 3D spatial rays and transforms them into global space.<n>PhysHMR produces high-fidelity, physically plausible motion across diverse scenarios, outperforming prior approaches in both visual accuracy and physical realism.
arXiv Detail & Related papers (2025-10-02T21:01:11Z) - A High-Force Gripper with Embedded Multimodal Sensing for Powerful and Perception Driven Grasping [12.351326520775464]
We develop a modular high grasping force gripper equipped with embedded multi-modal perception functionalities.<n>The proposed gripper can generate a grasping force of 110 N in a compact implementation.<n>The high grasping force capability is combined with embedded multi-modal sensing, which includes an eye-in-hand camera, a Time-of-Flight (ToF) distance sensor, an Inertial Measurement Unit (IMU) and an omnidirectional microphone.
arXiv Detail & Related papers (2025-04-07T11:57:08Z) - Reconfigurable Data Glove for Reconstructing Physical and Virtual Grasps [100.72245315180433]
We present a reconfigurable data glove design to capture different modes of human hand-object interactions.
The glove operates in three modes for various downstream tasks with distinct features.
We evaluate the system's three modes by (i) recording hand gestures and associated forces, (ii) improving manipulation fluency in VR, and (iii) producing realistic simulation effects of various tool uses.
arXiv Detail & Related papers (2023-01-14T05:35:50Z) - A background-free optically levitated charge sensor [50.591267188664666]
We introduce a new technique to model and eliminate dipole moment interactions limiting the performance of sensors employing levitated objects.
As a demonstration, this is applied to the search for unknown charges of a magnitude much below that of an electron.
As a by-product of the technique, the electromagnetic properties of the levitated objects can also be measured on an individual basis.
arXiv Detail & Related papers (2021-12-20T08:16:28Z) - Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform.
We produce a closed-loop controller to reactively push objects in a continuous action space.
We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
arXiv Detail & Related papers (2021-11-15T18:50:04Z) - Leveraging distributed contact force measurements for slip detection: a
physics-based approach enabled by a data-driven tactile sensor [5.027571997864706]
This paper describes a novel model-based slip detection pipeline that can predict possibly failing grasps in real-time.
A vision-based tactile sensor that accurately estimates distributed forces was integrated into a grasping setup composed of a six degrees-of-freedom cobot and a two-finger gripper.
Results show that the system can reliably predict slip while manipulating objects of different shapes, materials, and weights.
arXiv Detail & Related papers (2021-09-23T17:12:46Z) - Under Pressure: Learning to Detect Slip with Barometric Tactile Sensors [7.35805050004643]
We present a learning-based method to detect slip using barometric tactile sensors.
We are able to achieve slip detection accuracies of greater than 91%.
We show that barometric tactile sensing technology, combined with data-driven learning, is potentially suitable for many complex manipulation tasks.
arXiv Detail & Related papers (2021-03-24T19:29:03Z) - Zero-shot sim-to-real transfer of tactile control policies for
aggressive swing-up manipulation [5.027571997864706]
This paper shows that robots equipped with a vision-based tactile sensor can perform dynamic manipulation tasks without prior knowledge of all the physical attributes of the objects to be manipulated.
A robotic system is presented that is able to swing up poles of different masses, radii and lengths, to an angle of 180 degrees.
This is the first work where a feedback policy from high-dimensional tactile observations is used to control the swing-up manipulation of poles in closed-loop.
arXiv Detail & Related papers (2021-01-07T18:43:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.