United States

Human hand has amazing super-resolution ability in sensing the force and position of contact and this ability can be strengthened by practice. Inspired by this, we propose a method for robotic tactile super-resolution enhancement by learning spatiotemporal continuity of contact position and a tactile sensor composed of overlapping air chambers. Each overlapping air chamber is constructed of soft material and seals the barometer inside to mimic adapting receptors of human skin. Each barometer obtains the global receptive field of the contact surface with the pressure propagation in the hyperelastic seal overlapping air chambers. 
Neural networks with causal convolution are employed to resolve the pressure data sampled by barometers and to predict the contact position. The temporal consistency of spatial position contributes to the accuracy and stability of positioning. We obtain an average super-resolution (SR) factor of over 2500 with only four physical sensing nodes on the rubber surface (0.1 mm in the best case on 38 × 26 mm²), which outperforms the state-of-the-art. The effect of time series length on the location prediction accuracy of causal convolution is quantitatively analyzed in this article. 
We show that robots can accomplish challenging tasks such as haptic trajectory following, adaptive grasping, and human-robot interaction with the tactile sensor. This research provides new insight into tactile super-resolution sensing and could be beneficial to various applications in the robotics field. 

AAAI 2023

Improving Robotic Tactile Localization Super-Resolution via spatiotemporal Continuity Learning and Overlapping Air Chambers

causal convolution

spatiotemporal continuity learning

tactile

super-resolution

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

poster

Understanding the informative structures of scenes is essential for low-level vision tasks. Unfortunately, it is difficult to obtain a concrete visual definition of the informative structures because influences of visual features are task-specific.
In this paper, we propose a single general neural network architecture for extracting task-specific structure guidance for scenes. To do this, we first analyze traditional spectral clustering methods, which computes a set of eigenvectors to model a segmented graph forming small compact structures on image domains. We then unfold the traditional graph-partitioning problem into a learnable network, named Scene Structure Guidance Network (SSGNet), to represent the task-specific informative structures.  The SSGNet yields a set of coefficients of eigenvectors that produces explicit feature representations of image structures. In addition, our SSGNet is light-weight ($\sim$ 55K parameters), and can be used as a plug-and-play module for off-the-shelf architectures. We optimize the SSGNet without any supervision by proposing two novel training losses that enforce task-specific scene structure generation during training. Our main contribution is to show that such a simple network can achieve state-of-the art results for several low-level vision applications including joint upsampling and image denoising. Our submitted source codes also demonstrate that our SSGNet generalizes well on unseen datasets, compared to existing methods which use structural embedding frameworks.

Task-specific Scene Structure Representations

Understanding intention behind event processes in texts is important to many applications. One challenging task in this line is event process typing, which aims to tag the process with one action label and one object label describing the overall action of the process and object the process likely affects respectively. To tackle this task, existing methods mainly rely on the matching of the event process level and label level representation, which ignores two important characteristics: Process Hierarchy and Label Hierarchy. In this paper, we propose a Hierarchical Optimal Transport (HOT) method to address the above problem. Specifically, we first explicitly extract the process hierarchy and label hierarchy. Then the HOT optimally matches the two types of hierarchy. Experimental results demonstrate that our proposed model outperforms previous methods. In particular, the MRR is improved by 10.55% and the recall@10 is improved by 14.54% in action label prediction.

Event Process Typing via Hierarchical Optimal Transport

Time or money? That is a question! In this paper, we consider this dilemma in the pricing regime, in which we try to find the optimal pricing scheme for identical items with heterogenous time-sensitive buyers. We characterize the revenue-optimal solution and propose an efficient algorithm to find it in a Bayesian setting. Our results also demonstrate the tight ratio between the value of wasted time and the seller's revenue, as well as that of two common-used pricing schemes, the $k$-step function and the fixed pricing. To explore the nature of the optimal scheme in the general setting, we present the closed forms over the product distribution and show by examples that positive correlation between the valuation of the item and the cost per unit time could help increase revenue. To the best of our knowledge, it is the first step towards understanding the impact of the time factor as a part of the buyer cost in pricing problems, in the computational view.

Optimal Pricing Schemes for Identical Items with Time-Sensitive Buyers

The prevalence of region-based urban data has enabled us to explore the correlations of regions for better urban planning and smart-city solutions, in which region embedding plays an important role. However, this is a challenging problem considering the following two factors: different city data for region embedding and the effectiveness for various downstream applications. 
In this paper, we propose a novel framework, named \textbf{HREP} (\textbf{H}eterogeneous \textbf{R}egion \textbf{E}mbedding with \textbf{P}rompt learning), considering both intra-region and inter-region correlations, which includes two major modules, \textbf{H}eterogeneous graph learning for \textbf{R}egion \textbf{E}mbedding (\textbf{HRE}) and prompt learning for different downstream tasks. In the HRE module, we construct a region heterogeneous graph according to three categories of data, capturing inter-region contexts, e.g., human mobility and region geographic neighbors, and intra-region contexts, e.g., POI information. Specially, we design a \textit{relation-aware graph embedding} to learn region embedding and relation embeddings of edge types based on the heterogeneous graph. Then we introduce self-attention to capture the global correlation of regions. Moreover, we develop a \textit{attention-based fusion module} to integrate sharing information among different types of correlations. 
To enable region embedding to be effectively utilized in downstream tasks, we migrate the prompt learning, i.e., continuous prompt
prefix-tuning, which can guide the learning of downstream tasks, leading to better prediction results. Extensive experimental results on real-world datasets show that our model outperforms all state-of-the-art methods dramatically.

Heterogeneous Region Embedding with Prompt Learning

Attention mechanisms, such as local and non-local attention, play a fundamental role in recent deep learning based speech enhancement (SE) systems. However, a natural speech contains many fast-changing and relatively briefly acoustic events, therefore, capturing the most informative speech features by indiscriminately using local and non-local attention is challenged. We observe that the noise type and speech feature vary within a sequence of speech and the local and non-local can respectively process different types of corrupted speech regions. To leverage this, we propose Selector-Enhancer, a dual-attention based convolution neural network (CNN) with a feature-filter that can dynamically select regions from low-resolution speech features and feed them to local or non-local attention operations. In particular, the proposed feature-filter is trained by using reinforcement learning (RL) with a developed difficulty-regulated reward that related to network performance, model complexity and “the difficulty of the SE task”. The results show that our method achieves comparable or superior performance to existing approaches. In particular, Selector-Enhancer is effective for real-world denoising, where the number and types of noise are varies on a single noisy mixture.

Selector-Enhancer: Learning Dynamic Selection of Local and Non-local Attention Operation for Speech Enhancement

Despite success in many challenging problems, reinforcement learning (RL) is still confronted with sample inefficiency, which can be mitigated by introducing prior knowledge to agents. However, many transfer techniques in reinforcement learning make the limiting assumption that the teacher is an expert. In this paper, we use the action prior from the Reinforcement Learning as Inference framework - that is, a distribution over actions at each state which resembles a teacher policy, rather than a Bayesian prior - to recover state-of-the-art policy distillation techniques. Then, we propose a class of adaptive methods that can robustly exploit action priors by combining reward shaping and auxiliary regularization losses. In contrast to prior work, we develop algorithms for leveraging suboptimal action priors that may nevertheless impart valuable knowledge - which we call soft action priors. The proposed algorithms adapt by adjusting the strength of teacher feedback according to an estimate of the teacher's usefulness in each state. We perform tabular experiments, which show that the proposed methods achieve state-of-the-art performance, surpassing it when learning from suboptimal priors. Finally, we demonstrate the robustness of the adaptive algorithms in continuous action deep RL problems, in which adaptive algorithms considerably improved stability when compared to existing policy distillation methods.

Soft Action Priors: Towards Robust Policy Transfer

Satisfaction of the strict saddle property has become a standard assumption in non-convex optimization, and it ensures that many first-order optimization
algorithms will almost always escape saddle points. However, functions exist in machine learning that do not satisfy this property, such as the loss function of a neural network with at least two hidden layers. First-order methods such as gradient descent may converge to non-strict saddle points of such functions, and there do not currently exist any first-order methods that reliably escape non-strict saddle points. To address this need, we demonstrate that regularizing a function with a linear term enforces the strict saddle property, and we provide justification for only regularizing locally, i.e., when the norm of the gradient falls below a certain threshold. We analyze bifurcations that may result from this form of regularization, and then we provide a selection rule for regularizers that depends only on the gradient of an objective function. This rule is shown to guarantee that gradient descent will escape the neighborhoods around a broad class of non-strict saddle points, and this behavior is demonstrated on numerical examples of non-strict saddle points common in the optimization literature.

Linear Regularizers Enforce the Strict Saddle Property

Multimodal named entity recognition (MNER) is a critical step in information extraction, which aims to detect entity spans and classify them to corresponding entity types given a sentence-image pair. Existing methods either (1) obtain named entities with coarse-grained visual clues from attention mechanisms, or (2) first detect fine-grained visual regions with toolkits and then recognize named entities. However, they suffer from improper alignment between entity types and visual regions or error propagation in the two-stage manner, which finally imports irrelevant visual information into texts. In this paper, we propose a novel end-to-end framework named MNER-QG that can simultaneously perform MRC-based multimodal named entity recognition and query grounding. Specifically, with the assistance of queries, MNER-QG can provide prior knowledge of entity types and visual regions, and further enhance representations of both text and image. To conduct the query grounding task, we provide manual annotations and weak supervisions that are obtained via training a highly flexible visual grounding model with transfer learning. We conduct extensive experiments on two public MNER datasets, Twitter2015 and Twitter2017. Experimental results show that MNER-QG outperforms the current state-of-the-art models on the MNER task, and also improves the query grounding performance.

MNER-QG: An End-to-End MRC Framework for Multimodal Named Entity Recognition with Query Grounding

The opioid overdose epidemic represents a serious public health crisis, with fatality rates rising considerably over the past several years. To help address the abuse of prescription opioids, state governments collect data on dispensed prescriptions, yet the use of these data is typically limited to manual searches. In this paper, we propose a novel graph-based framework for detecting anomalous opioid prescribing patterns in state Prescription Drug Monitoring Program (PDMP) data, which could aid governments in deterring opioid diversion and abuse. Specifically, we seek to identify connected networks of opioid prescribers and dispensers who engage in high-risk and possibly illicit activity. We develop and apply a novel extension of the Non-Parametric Heterogeneous Graph Scan (NPHGS) to two years of de-identified PDMP data from the state of Kansas, and find that NPHGS identifies subgraphs that are significantly more anomalous than those detected by other graph-based methods. NPHGS also reveals clusters of potentially illicit activity, which may strengthen state law enforcement and regulatory capabilities. Our paper is the first to demonstrate how prescription data can systematically identify anomalous opioid prescribers and dispensers, as well as illustrating the efficacy of a network-based approach. Additionally, our technical extensions to NPHGS offer both improved flexibility and graph density reduction, enabling the framework to be replicated across jurisdictions and extended to other problem domains.

Improving Robotic Tactile Localization Super-Resolution via spatiotemporal Continuity Learning and Overlapping Air Chambers

Downloads

Next from AAAI 2023

Task-specific Scene Structure Representations

Similar lecture

GAN Prior based Null-Space Learning for Consistent Super-Resolution