ControlTac: Force- and Position-Controlled Tactile Data Augmentation with a Single Reference Image

• URL: http://arxiv.org/abs/2505.20498v2
• Date: Wed, 28 May 2025 02:10:51 GMT
• Title: ControlTac: Force- and Position-Controlled Tactile Data Augmentation with a Single Reference Image
• Authors: Dongyu Luo, Kelin Yu, Amir-Hossein Shahidzadeh, Cornelia Fermüller, Yiannis Aloimonos, Ruohan Gao,
• Abstract summary: ControlTac is a controllable framework that generates realistic tactile images conditioned on a single reference tactile image, contact force, and contact position.<n>We demonstrate that ControlTac can effectively augment tactile datasets and lead to consistent gains.
• Score: 24.223013595239916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-based tactile sensing has been widely used in perception, reconstruction, and robotic manipulation. However, collecting large-scale tactile data remains costly due to the localized nature of sensor-object interactions and inconsistencies across sensor instances. Existing approaches to scaling tactile data, such as simulation and free-form tactile generation, often suffer from unrealistic output and poor transferability to downstream tasks. To address this, we propose ControlTac, a two-stage controllable framework that generates realistic tactile images conditioned on a single reference tactile image, contact force, and contact position. With those physical priors as control input, ControlTac generates physically plausible and varied tactile images that can be used for effective data augmentation. Through experiments on three downstream tasks, we demonstrate that ControlTac can effectively augment tactile datasets and lead to consistent gains. Our three real-world experiments further validate the practical utility of our approach. Project page: https://dongyuluo.github.io/controltac.

Related papers

• Touch in the Wild: Learning Fine-Grained Manipulation with a Portable Visuo-Tactile Gripper [7.618517580705364]
  We present a portable, lightweight gripper with integrated tactile sensors.<n>We propose a cross-modal representation learning framework that integrates visual and tactile signals.<n>We validate our approach on fine-grained tasks such as test tube insertion and pipette-based fluid transfer.
  arXiv  Detail & Related papers  (2025-07-20T17:53:59Z)
• Towards Generalization of Tactile Image Generation: Reference-Free Evaluation in a Leakage-Free Setting [25.355424080824996]
  Tactile sensing is critical for human perception and underpins applications in computer vision, robotics, and multimodal learning.<n>Because tactile data is often scarce and costly to acquire, generating synthetic tactile images provides a scalable solution to augment real-world measurements.<n>We demonstrate that overlapping training and test samples in commonly used datasets inflate performance metrics, obscuring the true generalizability of tactile models.
  arXiv  Detail & Related papers  (2025-03-10T02:37:22Z)
• PseudoTouch: Efficiently Imaging the Surface Feel of Objects for Robotic Manipulation [8.997347199266592]
  We introduce PseudoTouch which links high-dimensional structural information to low-dimensional sensor signals.<n>It does so by learning a low-dimensional visual-tactile embedding, wherein we encode a depth patch from which we decode the tactile signal.<n>We demonstrate the utility of our trained PseudoTouch model in two downstream tasks: object recognition and grasp stability prediction.
  arXiv  Detail & Related papers  (2024-03-22T10:51:31Z)
• Multimodal and Force-Matched Imitation Learning with a See-Through Visuotactile Sensor [14.492202828369127]
  We leverage a multimodal visuotactile sensor within the framework of imitation learning (IL) to perform contact-rich tasks.<n>We introduce two algorithmic contributions, tactile force matching and learned mode switching, as complimentary methods for improving IL.<n>Our results show that the inclusion of force matching raises average policy success rates by 62.5%, visuotactile mode switching by 30.3%, and visuotactile data as a policy input by 42.5%.
  arXiv  Detail & Related papers  (2023-11-02T14:02:42Z)
• Controllable Visual-Tactile Synthesis [28.03469909285511]
  We develop a conditional generative model that synthesizes both visual and tactile outputs from a single sketch. We then introduce a pipeline to render high-quality visual and tactile outputs on an electroadhesion-based haptic device.
  arXiv  Detail & Related papers  (2023-05-04T17:59:51Z)
• Tactile-Filter: Interactive Tactile Perception for Part Mating [54.46221808805662]
  Humans rely on touch and tactile sensing for a lot of dexterous manipulation tasks. vision-based tactile sensors are being widely used for various robotic perception and control tasks. We present a method for interactive perception using vision-based tactile sensors for a part mating task.
  arXiv  Detail & Related papers  (2023-03-10T16:27:37Z)
• 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)
• Visual-Tactile Multimodality for Following Deformable Linear Objects Using Reinforcement Learning [15.758583731036007]
  We study the problem of using vision and tactile inputs together to complete the task of following deformable linear objects. We create a Reinforcement Learning agent using different sensing modalities and investigate how its behaviour can be boosted. Our experiments show that the use of both vision and tactile inputs, together with proprioception, allows the agent to complete the task in up to 92% of cases.
  arXiv  Detail & Related papers  (2022-03-31T21:59:08Z)
• Elastic Tactile Simulation Towards Tactile-Visual Perception [58.44106915440858]
  We propose Elastic Interaction of Particles (EIP) for tactile simulation. EIP models the tactile sensor as a group of coordinated particles, and the elastic property is applied to regulate the deformation of particles during contact. We further propose a tactile-visual perception network that enables information fusion between tactile data and visual images.
  arXiv  Detail & Related papers  (2021-08-11T03:49:59Z)
• Active 3D Shape Reconstruction from Vision and Touch [66.08432412497443]
  Humans build 3D understandings of the world through active object exploration, using jointly their senses of vision and touch. In 3D shape reconstruction, most recent progress has relied on static datasets of limited sensory data such as RGB images, depth maps or haptic readings. We introduce a system composed of: 1) a haptic simulator leveraging high spatial resolution vision-based tactile sensors for active touching of 3D objects; 2) a mesh-based 3D shape reconstruction model that relies on tactile or visuotactile priors to guide the shape exploration; and 3) a set of data-driven solutions with either tactile or visuo
  arXiv  Detail & Related papers  (2021-07-20T15:56:52Z)
• OmniTact: A Multi-Directional High Resolution Touch Sensor [109.28703530853542]
  Existing tactile sensors are either flat, have small sensitive fields or only provide low-resolution signals. We introduce OmniTact, a multi-directional high-resolution tactile sensor. We evaluate the capabilities of OmniTact on a challenging robotic control task.
  arXiv  Detail & Related papers  (2020-03-16T01:31:29Z)
• The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes? [57.366931129764815]
  We collect more than 9,000 grasping trials using a two-finger gripper equipped with GelSight high-resolution tactile sensors on each finger.<n>Our experimental results indicate that incorporating tactile readings substantially improve grasping performance.
  arXiv  Detail & Related papers  (2017-10-16T05:32:38Z)

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.