Search Results for author:
Found 23 papers, 4 papers with code
Leveraging Relaxed Equilibrium by Lazy Transition for Sequence Modeling
In sequence modeling, certain tokens are usually less ambiguous than others, and representations of these tokens require fewer refinements for disambiguation.
Vocabulary-informed Language Encoding
A Multilingual model relies on language encodings to identify input languages because the multilingual model has to distinguish between the input and output languages or among all the languages for cross-lingual tasks.
Tacchi 2.0: A Low Computational Cost and Comprehensive Dynamic Contact Simulator for Vision-based Tactile Sensors
To address these issues, we integrated a pinhole camera model into the low computational cost vision-based tactile simulator Tacchi that used the Material Point Method (MPM) as the simulated method, completing the simulation of marker motion images.
AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors
However, the distinct data characteristics of these low-standardized visuo-tactile sensors hinder the establishment of a powerful tactile perception system.
The establishment of static digital humans and the integration with spinal models
Next, we align the real spine model reconstructed from CT images with the standard skeletal model.
RoboBERT: An End-to-end Multimodal Robotic Manipulation Model
Embodied intelligence seamlessly integrates vision, language, and action.~However, most multimodal robotic models rely on massive fine-tuning, incurring high time and hardware costs.~To address this, we introduce RoboBERT, an end-to-end multimodal manipulation model built around a novel two-stage training paradigm.~In the first stage, we freeze most of the vision encoder and train with a single "standard" instruction phrasing, allowing the model to focus on stable policy learning via a CNN-based diffusion policy.~In the second stage, we unfreeze all modules and inject diverse natural language variants, rapidly aligning varied instructions to the already-learned policy without destabilizing performance.~We further employ systematic data augmentations to enhance robustness against visual perturbations.~Without relying on auxiliary datasets, RoboBERT achieves new state-of-the-art (SOTA) mean episode lengths of 4. 52 on the CALVIN ABCD-D benchmark and 3. 79 on the ABC-D benchmark using only language-labeled expert demonstrations and a comparatively lightweight architecture. Real-robot trials on a 6-DOF manipulator confirm higher success rates than comparable methods trained on identical data. These results demonstrate that our data-augmentation-enhanced two-stage training paradigm delivers efficient, scalable, and broadly applicable performance for multimodal robotic systems.
PIGUIQA: A Physical Imaging Guided Perceptual Framework for Underwater Image Quality Assessment
This module effectively captures subtle features in images, thereby enhancing the adaptive perception of distortions on the basis of local information.
Transformer in Touch: A Survey
The Transformer model, initially achieving significant success in the field of natural language processing, has recently shown great potential in the application of tactile perception.
Towards Comprehensive Multimodal Perception: Introducing the Touch-Language-Vision Dataset
Tactility provides crucial support and enhancement for the perception and interaction capabilities of both humans and robots.
A Lightweight Parallel Framework for Blind Image Quality Assessment
The batch-level quality comparison task is formulated to enhance the training data and thus improve the robustness of the latent representations.
Re-thinking Data Availability Attacks Against Deep Neural Networks
In this paper we re-examine the existing availability attack methods and propose a novel two-stage min-max-min optimization paradigm to generate robust unlearnable noise.
Towards Generalizable Data Protection With Transferable Unlearnable Examples
One of the crucial factors contributing to this success has been the access to an abundance of high-quality data for constructing machine learning models.
Re-thinking Data Availablity Attacks Against Deep Neural Networks
The unauthorized use of personal data for commercial purposes and the clandestine acquisition of private data for training machine learning models continue to raise concerns.
Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment
A composite robotic hand composed of a suction cup and a gripper is designed for grasping the object stably.
Transformer-based dimensionality reduction
Recently, Transformer is much popular and plays an important role in the fields of Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision (CV), etc.
ImmFusion: Robust mmWave-RGB Fusion for 3D Human Body Reconstruction in All Weather Conditions
However, combining RGB and mmWave signals for robust all-weather 3D human reconstruction is still an open challenge, given the sparse nature of mmWave and the vulnerability of RGB images.
Elastic Tactile Simulation Towards Tactile-Visual Perception
By contrast, 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.
Almost Free Semantic Draft for Neural Machine Translation
In this work, to inject global information but also save cost, we present an efficient method to sample and consider a semantic draft as global information from semantic space for decoding with almost free of cost.
Elastic Interaction of Particles for Robotic Tactile Simulation
At its core, EIP models the tactile sensor as a group of coordinated particles, and the elastic theory is applied to regulate the deformation of particles during the contact process.
Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation
The proposed architecture, termed as NICE-GAN, exhibits two advantageous patterns over previous approaches: First, it is more compact since no independent encoding component is required; Second, this plug-in encoder is directly trained by the adversary loss, making it more informative and trained more effectively if a multi-scale discriminator is applied.
Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance
In this paper, we study Reinforcement Learning from Demonstrations (RLfD) that improves the exploration efficiency of Reinforcement Learning (RL) by providing expert demonstrations.
Attention-based Transfer Learning for Brain-computer Interface
Different functional areas of the human brain play different roles in brain activity, which has not been paid sufficient research attention in the brain-computer interface (BCI) field.
PointNetGPD: Detecting Grasp Configurations from Point Sets
In this paper, we propose an end-to-end grasp evaluation model to address the challenging problem of localizing robot grasp configurations directly from the point cloud.
Robotics
