Symbiotic Backscatter Communication (SBC) has emerged as a spectrum-efficient and low-power communication technology, where backscatter devices (BDs) modulate and reflect incident radio frequency (RF) signals from primary transmitters (PTs). While previous studies have assumed a circularly symmetric complex Gaussian (CSCG) distribution for the BD’s signal, this assumption may not be practical because the high complexity of generating CSCG signals is not supported by the low-cost BD. In this paper, we address this gap by investigating SBC for two low-complexity modulation schemes, i.e.,M-ary amplitude-shift keying (MASK) andM-ary phase-shift keying (MPSK), where BD’s signals inherently deviate from CSCG distribution. Our goal is to derive the achievable rate of the PT and BD under the MASK/MPSK and to design MASK/MPSK modulation scheme for maximizing the PT’s rate. Towards this end, we first derive the expressions of both the PT’s rate and BD’s rate. Theoretical results reveal that whether or not the BD improves the PT’s rate depends on the phase of MASK/MPSK modulation, while the BD’s rate is independent of this phase. We then formulate two optimization problems to maximize the PT’s rate by adjusting the phase under the MASK and MPSK modulation schemes, respectively, and derive the optimal phases for each modulation scheme in closed forms. Given that the optimal phase is continuous and thus impractical for real-world BDs, we also propose a practical circuit design that enables BDs to select a discrete phase close to the theoretical optimum. Simulation results demonstrate that the optimal phase of MASK/MPSK can ensure an improvement in the PT’s rate and the performance gain between the ideal continuous phase and the practical implementation with few discrete phases is negligible, and reveal that a low-order ASK modulation is better than a low-order PSK for the BD in terms of improving PT’s rate, especially when the direct link is not significantly weaker than the backscatter link in SBC.

Symbiotic backscatter communication radio (SBCR) systems enable spectrum-efficient communication by allowing backscatter devices (BDs) to modulate and reflect primary transmitter (PT) signals. However, prior studies often assume circularly symmetric complex Gaussian (CSCG) signals for BDs, which is impractical for low-complexity BDs. This paper addresses this gap by investigating SBCR systems where the BD adopts one of two popular modulation schemes in wireless communications, i.e., binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK) modulation schemes. We derive the PT’s rates for these modulation schemes, highlighting the critical role of BD’s modulation phase in enhancing the PT’s rate. We then formulate two optimization problems to maximize the PT’s rate by optimizing the BD’s reflection phases for BPSK and QPSK, respectively, and derive closed-form expressions for the optimal phases. Simulations validate our theoretical analysis, and reveal that BPSK modulation achieves a higher PT’s rate than that of QPSK.

Natural language encodes rich sequential and contextual information. A task plan for robots can be extracted from natural language instruction through semantic understanding. This information includes sequential actions, target objects and descriptions of working environment. Current systems focus on single-domain understanding such as household or industrial assembly settings, and many rule-based approach have been developed in this context. Thanks to the development of deep learning, data-driven contextual language understanding shows promising results. In this work, an information extraction system is proposed for domain-independent understanding of robotic task plans. The developed approach is based on a pre-trained BERT-model and a syntactic dependency parser. To evaluate the performance, experiments are conducted on three different datasets.

In this paper, we introduce the problem of cross-modal visuo-tactile object recognition with robotic active exploration. With this term, we mean that the robot observes a set of objects with visual perception, and later on, it is able to recognize such objects only with tactile exploration, without having touched any object before. Using a machine learning terminology, in our application, we have a visual training set and a tactile test set, or vice versa. To tackle this problem, we propose an approach constituted by four steps: finding a visuo-tactile common representation, defining a suitable set of features, transferring the features across the domains, and classifying the objects. We show the results of our approach using a set of 15 objects, collecting 40 visual examples and five tactile examples for each object. The proposed approach achieves an accuracy of 94.7%, which is comparable with the accuracy of the monomodal case, i.e., when using visual data both as training set and test set. Moreover, it performs well compared to the human ability, which we have roughly estimated carrying out an experiment with ten participants.

In this work, we propose a framework to deal with cross-modal visuo-tactile object recognition. By cross-modal visuo-tactile object recognition, we mean that the object recognition algorithm is trained only with visual data and is able to recognize objects leveraging only tactile perception. The proposed cross-modal framework is constituted by three main elements. The first is a unified representation of visual and tactile data, which is suitable for cross-modal perception. The second is a set of features able to encode the chosen representation for classification applications. The third is a supervised learning algorithm, which takes advantage of the chosen descriptor. In order to show the results of our approach, we performed experiments with 15 objects common in domestic and industrial environments. Moreover, we compare the performance of the proposed framework with the performance of 10 humans in a simple cross-modal recognition task.