Safely Learning Visuo-Tactile Feedback Policies in Real For Industrial Insertion

Industrial insertion tasks are often performed repetitively with parts that are subject to tight tolerances and prone to breakage. In this paper, we present a safe method to learn a visuo-tactile insertion policy that is robust against grasp pose variations while minimizing human inputs and collision between the robot and the environment. We achieve this by dividing the insertion task into two phases. In the first align phase, we learn a tactile-based grasp pose estimation model to align the insertion part with the receptacle. In the second insert phase, we learn a vision-based policy to guide the part into the receptacle. Using force-torque sensing, we also develop a safe self-supervised data collection pipeline that limits collision between the part and the surrounding environment. Physical experiments on the USB insertion task from the NIST Assembly Taskboard suggest that our approach can achieve 45/45 insertion successes on 45 different initial grasp poses, improving on two baselines: (1) a behavior cloning agent trained on 50 human insertion demonstrations (1/45) and (2) an online RL policy (TD3) trained in real (0/45)

Self-NeRF: A Self-Training Pipeline for Few-Shot Neural Radiance Fields

Recently, Neural Radiance Fields (NeRF) have emerged as a potent method for synthesizing novel views from a dense set of images. Despite its impressive performance, NeRF is plagued by its necessity for numerous calibrated views and its accuracy diminishes significantly in a few-shot setting. To address this challenge, we propose Self-NeRF, a self-evolved NeRF that iteratively refines the radiance fields with very few number of input views, without incorporating additional priors. Basically, we train our model under the supervision of reference and unseen views simultaneously in an iterative procedure. In each iteration, we label unseen views with the predicted colors or warped pixels generated by the model from the preceding iteration. However, these expanded pseudo-views are afflicted by imprecision in color and warping artifacts, which degrades the performance of NeRF. To alleviate this issue, we construct an uncertainty-aware NeRF with specialized embeddings. Some techniques such as cone entropy regularization are further utilized to leverage the pseudo-views in the most efficient manner. Through experiments under various settings, we verified that our Self-NeRF is robust to input with uncertainty and surpasses existing methods when trained on limited training data.Comment: 11 pages, 11 figure

Evolution of Publications, Subjects, and Co-authorships in Network-On-Chip Research From a Complex Network Perspective

The academia and industry have been pursuing network-on-chip (NoC) related research since two decades ago when there was an urgency to respond to the scaling and technological challenges imposed on intra-chip communication in SoC designs. Like any other research topic, NoC inevitably goes through its life cycle: A. it started up (2000-2007) and quickly gained traction in its own right; B. it then entered the phase of growth and shakeout (2008-2013) with the research outcomes peaked in 2010 and remained high for another four/five years; C. NoC research was considered mature and stable (2014-2020), with signs showing a steady slowdown. Although from time to time, excellent survey articles on different subjects/aspects of NoC appeared in the open literature, yet there is no general consensus on where we are in this NoC roadmap and where we are heading, largely due to lack of an overarching methodology and tool to assess and quantify the research outcomes and evolution. In this paper, we address this issue from the perspective of three specific complex networks, namely the citation network, the subject citation network, and the co-authorship network. The network structure parameters (e.g., modularity, diameter, etc.) and graph dynamics of the three networks are extracted and analyzed, which helps reveal and explain the reasons and the driving forces behind all the changes observed in NoC research over 20 years. Additional analyses are performed in this study to link interesting phenomena surrounding the NoC area. They include: (1) relationships between communities in citation networks and NoC subjects, (2) measure and visualization of a subject\u27s influence score and its evolution, (3) knowledge flow among the six most popular NoC subjects and their relationships, (4) evolution of various subjects in terms of number of publications, (5) collaboration patterns and cross-community collaboration among the authors in NoC research, (6) interesting observation of career lifetime and productivity among NoC researchers, and finally (7) investigation of whether or not new authors are chasing hot subjects in NoC. All these analyses have led to a prediction of publications, subjects, and co-authorship in NoC research in the near future, which is also presented in the paper

A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration

Detection of Thermal Covert Channel Attacks Based on Classification of Components of the Thermal Signal Features

In response to growing security challenges facing many-core systems imposed by thermal covert channel (TCC) attacks, a number of threshold-based detection methods have been proposed. In this paper, we show that these threshold-based detection methods are inadequate to detect TCCs that harness advanced signaling and specific modulation techniques. Since the frequency representation of a TCC signal is found to have multiple side lobes, this important feature shall be explored to enhance the TCC detection capability. To this end, we present a pattern-classification-based TCC detection method using an artificial neural network that is trained with a large volume of spectrum traces of TCC signals. After proper training, this classifier is applied at runtime to infer TCCs, should they exist. The proposed detection method is able to achieve a detection accuracy of 99%, even in the presence of the stealthiest TCCs ever discovered. Because of its low runtime overhead (<0.187%) and low energy overhead (<0.072%), this proposed detection method can be indispensable in fighting against TCC attacks in many-core systems. With such a high accuracy in detecting TCCs, powerful countermeasures, like the ones based on dynamic voltage and frequency scaling (DVFS), can be rightfully applied to neutralize any malicious core participating in a TCC attack

On Evaluation of On-chip Thermal Covert Channel Attacks

hermal covert channel (TCC) attacks have been a serious security concern to the use of many-core chips. Severity of these attacks is directly linked to the TCC's transmission rate and its BER (bit error rate) performance, both of which are impacted by the transmission characteristics of thermal signals and adopted encoding, modulation, and multiplexing schemes. This paper examines, compares, and analyzes various TCCs built upon different combinations of encoding, modulation, and multiplexing. In particular, our study shows that TCC using non-return-to-zero (NRZ) line coding and frequency shift keying (FSK) modulation achieves the highest throughput of 120 bps and BER of below 10%

Combating Stealthy Thermal Covert Channel Attack With Its Thermal Signal Transmitted in Direct Sequence Spread Spectrum

Many-core systems are susceptible to attacks launched by thermal covert channel (TCC) attacks. Detection of TCC attacks often relies on the use of threshold-based approaches or variants, and a countermeasure to thwart the channel can be applied only after an attack is deemed to be present. In this article, we describe a direct sequence spread spectrum (DSSS)-based TCC, where its thermal data are modulated by a pseudo-random bit sequence. Unfortunately, such DSSS-based TCC has an extremely low signal strength that the signal is nearly indistinguishable from the noise and thus cannot be detected by any existing threshold-based detection methods. To combat this stealthy TCC, we propose a novel detection scheme that lets the received signal pass through a differential filter where irrelevant frequency components occupied mainly by the noise gets eliminated and the filtered signal is next compared against a threshold for successful detection. Experimental results show that the DSSS-based TCC can effectively survive detection by the existing detection methods with its BER as low as 4%. In contrast, with the proposed detection and countermeasure applied, the detection accuracy jumps to 89%, and the BER of the DSSS-based TCC soars to 50%, which indicates that the TCC is practically shut down

Nanofat lysate ameliorates pain and cartilage degradation of osteoarthritis through activation of TGF-β–Smad2/3 signaling of chondrocytes

Introduction: Nanofat is an effective cell therapy for osteoarthritis (OA). However, it has clinical limitations due to its short half-life. We developed Nanofat lysate (NFL) to overcome the defect of Nanofat and explore its anti-OA efficacy and mechanism.Methods: Monoiodoacetate (MIA) was employed to establish rat OA model. For pain assessment, paw withdrawal latency (PWL) and thermal withdrawal latency (TWL) were evaluated. Degeneration of cartilage was observed by histopathological and immunohistochemical examination. Primary chondrocytes were treated with TNF-α to establish the cellular model of OA. MTT, wound healing, and transwell assays were performed to assess effects of NFL on chondrocytes. RNA-seq, qPCR and Western blot assays were conducted to clarify the mechanism of NFL.Results and Discussion: The animal data showed that PWL and TWL values, Mankin’s and OARSI scorings, and the Col2 expression in cartilage were significantly improved in the NFL-treated OA rats. The cellular data showed that NFL significantly improved the proliferation, wound healing, and migration of chondrocytes. The molecular data showed that NFL significantly restored the TNF-α-altered anabolic markers (Sox9, Col2 and ACAN) and catabolic markers (IL6 and Mmp13). The RNA-seq identified that TGF-β-Smad2/3 signaling pathway mediated the efficacy of NFL, which was verified by qPCR and Western blot that NFL significantly restored the abnormal expressions of TGFβR2, phosphorylated-Smad2, phosphorylated-Smad2/3, Col2, Mmp13 and Mmp3. After long-term storage, NFL exerted similar effects as its fresh type, indicating its advantage of storability. In sum, NFL was developed as a new therapeutic approach and its anti-OA efficacy and mechanism that mediated by TGF-β-Smad2/3 signaling was determined for the first time. Besides, the storability of NFL provided a substantial advantage than other living cell-based therapies

Secured Data Transmission Over Insecure Networks-on-Chip by Modulating Inter-Packet Delays

As the network-on-chip (NoC) integrated into an SoC design can come from an untrusted third party, there is a growing risk that data integrity and security get compromised when supposedly sensitive data flows through such an untrusted NoC. We thus introduce a new method that can ensure secure and secret data transmission over such an untrusted NoC. Essentially, the proposed scheme relies on encoding binary data as delays between packets travelling across the source and destination pair. The maximum data transmission rate of this inter-packet-delay (IPD)-based communication channel can be determined from the analytical model developed in this article. To further improve the undetectability and robustness of the proposed data transmission scheme, a new block coding method and communication protocol are also proposed. Experimental results show that the proposed IPD-based method can achieve a packet error rate (PER) of as low as 0.3% and an effective throughput of $\boldsymbol {2.3\times 10^{5}}$ b/s, outperforming the methods of thermal covert channel, cache covert channel, and circuit-based encryption and, thus, is suitable for secure data transmission in unsecure systems

Design Challenges of Intra- and Inter- Chiplet Interconnection

In a chiplet-based many-core system, intra- and inter- chiplet interconnection is key to system performance and power consumption. There are a few challenges in intra- and inter- chiplet interconnection network: 1) Fast and accurate simulation is necessary to analyze the performance metrics. 2) Efficient network architecture for inter- and intra- chiplet is necessary, including topology, PHY design and deadlock free routing algorithms, etc. 3) Deep learning based AI systems are demanding more computation power, which calls for the need of efficient and low power chiplet-based systems. This paper proposes network designs to address these challenges and provides future research directions