6 research outputs found
Visual Tactile Fusion Object Clustering
Object clustering, aiming at grouping similar objects into one cluster with
an unsupervised strategy, has been extensivelystudied among various data-driven
applications. However, most existing state-of-the-art object clustering methods
(e.g., single-view or multi-view clustering methods) only explore visual
information, while ignoring one of most important sensing modalities, i.e.,
tactile information which can help capture different object properties and
further boost the performance of object clustering task. To effectively benefit
both visual and tactile modalities for object clustering, in this paper, we
propose a deep Auto-Encoder-like Non-negative Matrix Factorization framework
for visual-tactile fusion clustering. Specifically, deep matrix factorization
constrained by an under-complete Auto-Encoder-like architecture is employed to
jointly learn hierarchical expression of visual-tactile fusion data, and
preserve the local structure of data generating distribution of visual and
tactile modalities. Meanwhile, a graph regularizer is introduced to capture the
intrinsic relations of data samples within each modality. Furthermore, we
propose a modality-level consensus regularizer to effectively align thevisual
and tactile data in a common subspace in which the gap between visual and
tactile data is mitigated. For the model optimization, we present an efficient
alternating minimization strategy to solve our proposed model. Finally, we
conduct extensive experiments on public datasets to verify the effectiveness of
our framework.Comment: 8 pages, 5 figure
Automatic Curriculum Learning With Over-repetition Penalty for Dialogue Policy Learning
Dialogue policy learning based on reinforcement learning is difficult to be
applied to real users to train dialogue agents from scratch because of the high
cost. User simulators, which choose random user goals for the dialogue agent to
train on, have been considered as an affordable substitute for real users.
However, this random sampling method ignores the law of human learning, making
the learned dialogue policy inefficient and unstable. We propose a novel
framework, Automatic Curriculum Learning-based Deep Q-Network (ACL-DQN), which
replaces the traditional random sampling method with a teacher policy model to
realize the dialogue policy for automatic curriculum learning. The teacher
model arranges a meaningful ordered curriculum and automatically adjusts it by
monitoring the learning progress of the dialogue agent and the over-repetition
penalty without any requirement of prior knowledge. The learning progress of
the dialogue agent reflects the relationship between the dialogue agent's
ability and the sampled goals' difficulty for sample efficiency. The
over-repetition penalty guarantees the sampled diversity. Experiments show that
the ACL-DQN significantly improves the effectiveness and stability of dialogue
tasks with a statistically significant margin. Furthermore, the framework can
be further improved by equipping with different curriculum schedules, which
demonstrates that the framework has strong generalizability
I3DOL: Incremental 3D Object Learning without Catastrophic Forgetting
3D object classification has attracted appealing attentions in academic
researches and industrial applications. However, most existing methods need to
access the training data of past 3D object classes when facing the common
real-world scenario: new classes of 3D objects arrive in a sequence. Moreover,
the performance of advanced approaches degrades dramatically for past learned
classes (i.e., catastrophic forgetting), due to the irregular and redundant
geometric structures of 3D point cloud data. To address these challenges, we
propose a new Incremental 3D Object Learning (i.e., I3DOL) model, which is the
first exploration to learn new classes of 3D object continually. Specifically,
an adaptive-geometric centroid module is designed to construct discriminative
local geometric structures, which can better characterize the irregular point
cloud representation for 3D object. Afterwards, to prevent the catastrophic
forgetting brought by redundant geometric information, a geometric-aware
attention mechanism is developed to quantify the contributions of local
geometric structures, and explore unique 3D geometric characteristics with high
contributions for classes incremental learning. Meanwhile, a score fairness
compensation strategy is proposed to further alleviate the catastrophic
forgetting caused by unbalanced data between past and new classes of 3D object,
by compensating biased prediction for new classes in the validation phase.
Experiments on 3D representative datasets validate the superiority of our I3DOL
framework.Comment: Accepted by Association for the Advancement of Artificial
Intelligence 2021 (AAAI 2021
Adaptive visual–tactile fusion recognition for robotic operation of multi-material system
The use of robots in various industries is evolving from mechanization to intelligence and precision. These systems often comprise parts made of different materials and thus require accurate and comprehensive target identification. While humans perceive the world through a highly diverse perceptual system and can rapidly identify deformable objects through vision and touch to prevent slipping or excessive deformation during grasping, robot recognition technology mainly relies on visual sensors, which lack critical information such as object material, leading to incomplete cognition. Therefore, multimodal information fusion is believed to be key to the development of robot recognition. Firstly, a method of converting tactile sequences to images is proposed to deal with the obstacles of information exchange between different modalities for vision and touch, which overcomes the problems of the noise and instability of tactile data. Subsequently, a visual-tactile fusion network framework based on an adaptive dropout algorithm is constructed, together with an optimal joint mechanism between visual information and tactile information established, to solve the problem of mutual exclusion or unbalanced fusion in traditional fusion methods. Finally, experiments show that the proposed method effectively improves robot recognition ability, and the classification accuracy is as high as 99.3%