Providing a robot with the capabilities of understanding and effectively adapting its behaviour based on human mental states is a critical challenge in Human–Robot Interaction, since it can significantly improve the quality of interaction between humans and robots. In this work, we investigate whether considering human mental states in the decision-making process of a robot improves the transparency of its behaviours and mitigates potential human’s false beliefs about the environment during collaborative scenarios. We used Bayesian inference within a Hierarchical Reinforcement Learning algorithm to include human desires and beliefs into the decision-making processes of the robot, and to monitor the robot’s decisions. This approach, which we refer to as Hierarchical Bayesian Theory of Mind, represents an upgraded version of the initial Bayesian Theory of Mind, a probabilistic model capable of reasoning about a rational agent’s actions. The model enabled us to track the mental states of a human observer, even when the observer held false beliefs, thereby benefiting the collaboration in a multi-goal task and the interaction with the robot. In addition to a qualitative evaluation, we conducted a between-subjects study (110 participants) to evaluate the robot’s perceived Theory of Mind and its effects on transparency and false beliefs in different settings. Results indicate that a robot which considers human desires and beliefs increases its transparency and reduces misunderstandings. These findings show the importance of endowing Theory of Mind capabilities in robots and demonstrate how these skills can enhance their behaviours, particularly in human–robot collaboration, paving the way for more effective robotic applications.

For social robots to collaborate effectively, they must infer and correct false human beliefs, especially when misconceptions directly impact task outcomes or pose safety risks to humans. In this work, we investigated whether a robot’s ability to detect and rectify users’ false beliefs improves trust and perceived social competence. In an in-person between-subject study, 98 participants collaborated with a robot to solve a task. Participants interacted with either a robot that actively corrected their false beliefs by using Theory of Mind or one that complied with their incorrect instructions. Contrary to expectations, trust, mental state attribution, and perceived warmth or competence did not differ between groups. The results also showed that human reluctance to trust the robot’s input persisted, suggesting that belief correction alone cannot overcome relational barriers. In addition, the study showed that participants who trusted the robot’s corrections perceived it as more socially attuned.

In this work, we investigate whether the transparency of a robot’s behaviour is improved when human preferences on the actions the robot performs are taken into account during the learning process . For this purpose, a shielding mechanism called Preference Shielding is proposed and included in a reinforcement learning algorithm to account for human preferences. We also use the shielding to decide when to provide explanations of the robot’s actions. We carried out a within-subjects study involving 26 participants to evaluate the robot’s transparency. Results indicate that considering human preferences during learning improves legibility compared with providing only explanations. In addition, combining human preferences and explanations further amplifies transparency. Results also confirm that increased transparency leads to an increase in people’s perception of the robot’s safety, comfort, and reliability. These findings show the importance of transparency during learning and suggest a paradigm for robotic applications when a robot has to learn a task in the presence of or in collaboration with a human.

The lack of transparency in robotic learning processes poses a significant challenge to effective human-robot collaboration. This is particularly relevant in non-industrial settings because it prevents humans from adequately comprehending a robot’s intentions, progress, and decision-making rationale, which is essential for seamless interaction. To address this issue, this work presents a study where users observe a robot endowed with three distinct emotional/behavioural mechanisms for conveying transparent information about its learning process. The proposed mechanisms use inner speech, emotions, and a combination of the two communication styles (hybrid). To assess and evaluate the transparency of these behavioural models, a between-subject study was conducted with 108 participants. Results indicate that the people’s perception of the robot’s warmth dimension increased when it utilized a hybrid model to explain its learning state. Additionally, increased transparency was observed when the robot used inner speech during the learning process.

People and robots may need to cross each other in narrow spaces when they are sharing environments. It is expected that autonomous robots will behave in these contexts safely but also show social behaviors. Thereby, developing an acceptable behavior for autonomous robots in the area mentioned above is a foreseeable problem for the Human-Robot Interaction (HRI) field. Our current work focuses on integrating legible non-verbal behaviors into the robot's social navigation to make nearby humans aware of its intended trajectory. Results from a within-subjects study involving 33 participants show that deictic gestures as navigational cues for humanoid robots result in fewer navigation conflicts than the use of a simulated gaze. Additionally, an increase in the perceived anthropomorphism is found when the robot uses the deictic gesture as a cue. These findings show the importance of social behaviors for people avoidance and suggest a paradigm of such behaviors in future humanoid robotic applications.

When navigating in a shared environment, the extent to which robots are able to effectively use signals for coordinating with human behaviors can ameliorate dissatisfaction and increase acceptance. In this paper, we present an online video study to investigate whether familiar acoustic signals can improve the legibility of a robot’s navigation behavior. We collected the responses of 120 participants to evaluate their perceptions of a robot that communicates with one of the three used non-verbal navigational cues (an acoustic signal, an acoustic in pair with a visual signal, and a dissimilar frequency acoustic signal). Our results showed a significant legibility improvement when the robot used both light and acoustic signals to communicate its intentions compared to using only the same acoustic sound. Additionally, our findings highlighted that people also perceived differently the robot’s intentions when they were expressed through two frequencies of the mere sound. The results of this work suggest a paradigm that can help the development of mobile service robots in public spaces.

This paper proposes a partial packet recovery scheme called packetized rateless algebraic consistency (PRAC). PRAC exploits intra- and inter-packet consistency to identify and recover erroneous packet segments, without recourse to soft physical layer (PHY) or detailed feedback information. PRAC uses a rateless linear code for data encoding and an iterative decoding process for data reconstruction. It allows, but does not rely upon, the use of any PHY forward error correction code, and requires no feedback other than a notification of completion and, in the absence of partial packets, incurs no overhead. In order to quantify PRAC's performance in terms of both throughput and energy efficiency, experiments are conducted using commercial transceivers in two different scenarios. Our implementation results reveal that PRAC offers an average throughput gain of 35% compared with a baseline ARQ scheme discarding partial packets, and 13% compared with an ideal hybrid-ARQ scheme. On high PER links, throughput is improved by 148% and 34%, respectively. In addition, PRAC reduces on average the total energy consumption of the transmitting nodes by 16%, while, on high PER links, savings can be up to 50%.

This paper introduces AdaptCast, an integrated source to transmission scheme for wireless sensor networks (WSNs) that efficiently represents collected data and increases their robustness against channel errors across a wide range of signal to noise (SNR) values in a rateless fashion. AdaptCast leverages sparsity inherent in the majority of physical signals in order to parsimoniously represent them without relying on a specific signal model. The proposed scheme does not suffer from the sudden degradation in the tradeoff between distortion and SNR of rated channel coding schemes due to its direct, relative bit importance preserving modulation mapping. In addition, it does not require continuous feedback or channel state information (CSI) as a result of its rateless operation. Apart from point-to-point transmission, AdaptCast enables efficient multicasting to a set of nodes, serving each of them at a rate commensurate to its individual channel quality. We demonstrate AdaptCast's application-independent operation by using several typical signals captured in WSNs. Based on our analysis and simulation results, considering the tradeoff between distortion and channel quality, AdaptCast performs close in a point-to-point scenario to an idealized layered transmission scheme with instantaneous CSI and offers significant benefits in multiuser settings.

This paper proposes a partial packet recovery scheme, called Packetized Rateless Algebraic Consistency (PRAC). PRAC exploits intra and inter-packet consistency to identify and recover erroneous packet segments, without recourse to cross-layer or detailed feedback information. In the absence of cross-layer coordination or detailed feedback, the prevailing methods proposed in the literature have discarded packets with errors. PRAC uses a rateless linear packet code for data encoding and an iterative decoding process consisting of a search algorithm and an algebraic consistency rule (ACR) check. It allows, but not relies upon, the use of any PHY FEC code, requires no feedback other than a notification of completion and, in the absence of partial packets, incurs no overhead. Our implementation and experimental results in a 7-node indoor testbed using wireless boards equipped with CC2500 radio transceivers reveal that PRAC offers an average throughput gain of 35% compared to a baseline ARQ scheme discarding partial packets and 13% compared to an ideal genie-aided HARQ (iHARQ) scheme. Specifically for links with high PERs, PRAC significantly enhances their robustness and its maximum throughput gain is 148% and 34% compared against the baseline and iHARQ schemes, respectively.

In this paper, we evaluate the performance of random linear network coding (RLNC) in low data rate indoor sensor applications operating in the ISM frequency band. We also investigate the results of its synergy with forward error correction (FEC) codes at the PHY-layer in a joint channel-network coding (JCNC) scheme. RLNC is an emerging coding technique which can be used as a packet-level erasure code, usually implemented at the network layer, which increases data reliability against channel fading and severe interference, while FEC codes are mainly used for correction of random bit errors within a received packet. The hostile wireless environment that low power sensors usually operate in, with significant interference from nearby networks, motivates us to consider a joint coding scheme and examine the applicability of RLNC as an erasure code in such a coding structure. Our analysis and experiments are performed using a custom low power sensor node, which integrates on-chip a low-power 2.4 GHz transmitter and an accelerator implementing a multi-rate convolutional code and RLNC, in a typical office environment. According to measurement results, RLNC of code rate 4/8 can provide an effective SNR improvement of about 3.4 dB, outperforming a PHY-layer FEC code of the same code rate, at a PER of 10-2. In addition, RLNC performs very well when used in conjunction with a PHY-layer FEC code as a JCNC scheme, offering an overall coding gain of 5.6 dB.

Parallel algorithms on barrel shifter computers for a broad class of 1-D and 2-D signal operators are presented in this paper. Running max/min selection filter, moving average filter and sorting, which can be used in order statistics filtering, are examined. The proposed algorithms require a significantly smaller number of comparisons/computations per output point than the conventional ones. They are suitable to be implemented on barrel shifter computer topologies.

This paper presents a parallel algorithm for the calculation of vector median filters on mesh connected computers. A straightforward algorithm to find the vector median requires the computation of the sum of the distances (norms) of each vector to all other vectors in the filter window. By taking into account the fact that adjacent filter windows share common input samples, the computation of the common norms of adjacent windows could be done only once. In the proposed algorithm, a 2-D signal is assumed to be stored in a SIMD mesh connected computer of the same size. According to this scheme, each processing element (PE) computes some norms and the rest ones, that are required for the selection of the vector median, are received from its neighbouring PEs via the local communication channels.>

This paper deals with the implementation of a fast algorithm for two-dimensional weighted median filtering. Because of the vast amount of data that must be handled, the development of fast algorithms is very important. A fast running algorithm for weighted median filtering, which is based on using a histogram and updating it, is proposed. Experimental results prove the superiority of the proposed algorithm over that of finding the weighted median either by sorting with the Quick Sort or by selecting the r-th order statistic.

Deals with the design of multichannel Wiener filters both in the spatial and frequency domain. FIR and IIR Wiener filters are presented. Color image restoration based on multi-channel autoregressive (AR) image modeling is examined. Detailed discussions on the use of a multichannel Wiener filter in color image restoration incorporating the interchannel correlations and computer simulations are presented also.>

Color images are multichannel signals, whereas monochrome images are single-channel signals. Therefore, the extension of techniques used in monochrome digital restoration to color images is not straightforward. The interchannel correlations which exist in color images must be taken into account in the restoration procedure. The design of a multichannel Wiener filter in the frequency domain is presented. The sensitivity of the multichannel Wiener filter to the spectral estimates requires the use of a robust multichannel power spectral estimator. The use of multichannel autoregressive (AR) spectral estimators results in multichannel filters which have excellent performance.>