In this paper, we present our focus on unraveling the intricacies of plan negotiation in human-robot collaborative navigation (HRCN) through a comprehensive exploration of human preferences over robot proposals in search tasks. Via online survey data, we explore the multidimensional landscape of diverse plan representations, negotiation contexts and negotiation domains. Our study seeks to identify the crucial factors that exert a significant influence over human perception, shedding light on the dynamic interplay between humans and robots and contributing valuable insights to advance the understanding of effective navigation plan negotiation strategies in human-robot teams (HRT).

Recent research in Human Robot Collaboration (HRC) has spread and specialised in many sub-fields. Many show considerable advances, but the human–robot collaborative navigation (HRCN) field seems to be stuck focusing on implicit collaboration settings, on hypothetical or simulated task allocation problems, on shared autonomy or on having the human as a manager. This work takes a step forward by presenting an end-to-end system capable of handling real-world human–robot collaborative navigation tasks. This system makes use of the Social Reward Sources model (SRS), a knowledge representation to simultaneously tackle task allocation and path planning, proposes a multi-agent Monte Carlo Tree Search (MCTS) planner for human–robot teams, presents the collaborative search as a testbed for HRCN and studies the usage of smartphones for communication in this setting. The detailed experiments prove the viability of the approach, explore collaboration roles adopted by the human–robot team and test the acceptability and utility of different communication interface designs.

Assistive robots need to be able to perform a large number of tasks that imply some type of cloth manipulation. These tasks include domestic chores such as laundry handling or bed-making, among others, as well as dressing assistance to disabled users. Due to the deformable nature of fabrics, this manipulation requires a strong perceptual feedback. Common perceptual skills that enable robots to complete their cloth manipulation tasks are reviewed here, mainly relying on vision, but also resorting to touch and force. The use of such basic skills is then examined in the context of the different cloth manipulation tasks, be them garment-only applications in the line of performing domestic chores, or involving physical contact with a human as in dressing assistance.

This paper proposes a Social Reward Sources (SRS) design for a Human-Robot Collaborative Navigation (HRCN) task: human-robot collaborative search. It is a flexible approach capable of handling the collaborative task, human-robot interaction and environment restrictions, all integrated on a common environment. We modelled task rewards based on unexplored area observability and isolation and evaluated the model through different levels of human-robot communication. The models are validated through quantitative evaluation against both agents' individual performance and qualitative surveying of participants' perception. After that, the three proposed communication levels are compared against each other using the previous metrics.

To meet current market demands as well as emerging environmental concerns there is a need to develop less polluting battery technologies.

In order to meet the challenge of a transition from fossil fuels to green energies, the rapid development of technology is crucial. A characteristic of energy produced from renewable sources is to be discontinuous in time (e. g. photovoltaic or wind turbines). So, as a result of the development of this kind of systems, it is really important to establish a reliable electric energy storage grid. Nowadays, advanced devices that convert and store energy are the focus of intensive attentions and lithium-ion batteries hold the promise of effective solution for a clean electric future. Nevertheless, the needs to improve performances and reduce costs of Li-ion batteries encourage different research strategies. Among them, an increase in the basis weight of the electrodes active material allows to improve the energy density and to reduce the costs associated with the electrochemical inactive materials (current collectors and separator). The maximum loading achievable by the current industrial reference process, the coating, is limited by the migration of the binder during solvent evaporation phase. Thus, an innovative and effective process, the filtration, is here proposed for the production of ultra-thick electrodes (> 20 mAh / cm² or > 50 mg / cm²). In particular, this research is focused on the adaptation of filtration with the aim to obtain Li-ion battery electrodes and on the development of a pseudo 3D current collector. This innovative current collector is suitable for the filtration process, participates in the transversal electrical conductivity and improves the adhesion of the active layer. The active materials used in this research are commercial LiNiMnCo (NMC) for the cathode and graphite for the anode. The mechanical properties of ultra-thick electrodes are, of course, critical and will be discussed. Moreover, the remarkable mechanical stability along with the improved adhesion of active material layer with the metallic current collector allows electrodes to be pressed and to attain low porosity, resulting in improved energy density and increased electrical conductivity due to a better contact between particles. The electrochemical performances of electrodes based on NMC/graphite and of a full cells composed of these ultra-thick electrodes will be presented. These ultra-thick electrodes feature a volumetric energy density greater than the state of art Li-ion electrodes, making them particularly suitable for stock energy produced from renewable and natural sources, giving the possibility to increase the capacity of future electricity grid. Fig. 1: Ambient temperature galvanostatic cycling behavior of a NMC/graphite ultra-thick cell at different C-rate. Inset shows typical potential vs. specific capacity profile. Figure 1

Recent developments at IMN will be shared on several research directions out of usual paths for performance improvement of Li-ion batteries. We will focus on innovative surface modifications of electrode components, new electrode compositions and architectures, and failure mechanism upon cycling by in-depth characterization through coupled advanced spectroscopic techniques. A molecular grafting approach has been proposed as a way to modify the interfacial chemical reactivity of oxide materials, which is detrimental to their long-term energy storage properties as electrodes of Li-ion batteries. Surface derivatization of powder oxide materials such as Li 1.2 V 3 O 8 and Li(Mn,Ni) 2 O 4 was accomplished by in situ electrografting of a diazonium salt during Li-ion intercalation, leading to a covalently bonded organic multilayer. Charge transfer is not impeded, while electrolyte decomposition is inhibited thus increasing the cycle life and decreasing the self-discharge. Carbon additives of classical porous electrodes occupy a large volume fraction which is lost for charge storage. Redox functionalization of the surface of some carbon additives has been successfully achieved through non-covalent grafting chemistry using multi-redox pyrene molecules synthesized on purpose. Such functionalized carbon additives have been used to increase the stored energy and power of C-coated LFP porous electrodes. Thicker electrodes are needed for higher energy density Li-ion batteries. We evaluate different directions in order to design new innovative electrode architectures for such a purpose. Our grafting chemistry has been further developed to achieve molecular junctions between non-carbon-coated LFP and multiwall carbon nanotubes (MWCNT) using a designed thiophene-based conjugated molecule. The strategy enables original architecturing of the cathode of Li-ion batteries, with the individual MWCNT being electronically nanocontacted at the surface of LFP grains. This advancement leads to much higher specific capacity and better capacity retention for non calendared thick electrodes, for which the electronic wiring of the electroactive material grains is a critical issue. Another direction followed is the use of conducting polymer additives in porous electrodes, which are able to act as both conducting fillers and mechanical reinforcement materials. We have synthesized a new form of lithium doped PANI, the excellent properties of which in terms of specific capacity, stability on cycling and rate capability will be presented. The coating of bare LFP particles with thin layers of this new Li-doped PANI allows surpassing the performance of commercial carbon coated LFP thick electrodes. The role of this PANI additive into millimetric thick electrodes of NMC material will also be discussed. Future developments of higher energy density Si-based Li-ion batteries depend on the mastering of side reactions at the Si anode. We will compare the SEI composition and morphology at the Si surface upon cycling in half cell and full Li-ion cell configurations using a combination of 7 Li, 19 F MAS NMR, XPS, TOF-SIMS and STEM-EELS. The origin of the much faster aging of Si-based full cells versus half cells and future directions for improvement will be discussed

Robots are becoming safe and smart enough to work alongside people not only on manufacturing production lines, but also in spaces such as houses, museums, or hospitals. This can be significantly exploited in situations in which a human needs the help of another person to perform a task, because a robot may take the role of the helper. In this sense, a human and the robotic assistant may cooperatively carry out a variety of tasks, therefore requiring the robot to communicate with the person, understand his/her needs, and behave accordingly. To achieve this, we propose a framework for a user to teach a robot collaborative skills from demonstrations. We mainly focus on tasks involving physical contact with the user, in which not only position, but also force sensing and compliance become highly relevant. Specifically, we present an approach that combines probabilistic learning, dynamical systems, and stiffness estimation to encode the robot behavior along the task. Our method allows a robot to learn not only trajectory following skills, but also impedance behaviors. To show the functionality and flexibility of our approach, two different testbeds are used: a transportation task and a collaborative table assembly.

By using a set of advanced techniques such as the broad band dielectric spectroscopy, X-Ray and FIB-SEM computed tomography, it will be described which factor(s) (material properties, engineering parameters) mainly influence the rate discharging behavior of NMC- or LFP-based composite electrodes. This fundamental understanding will be rationalized to disclose ultra-high surface capacities electrodes (more than 10 mAh / cm²) with reasonable rate performance.

We describe a system allowing a robot to learn goal-directed manipulation sequences such as steps of an assembly task. Learning is based on a free mix of exploration and instruction by an external teacher, and may be active in the sense that the system tests actions to maximize learning progress and asks the teacher if needed. The main component is a symbolic planning engine that operates on learned rules, defined by actions and their pre- and postconditions. Learned by model-based reinforcement learning, rules are immediately available for planning. Thus, there are no distinct learning and application phases. We show how dynamic plans, replanned after every action if necessary, can be used for automatic execution of manipulation sequences, for monitoring of observed manipulation sequences, or a mix of the two, all while extending and refining the rule base on the fly. Quantitative results indicate fast convergence using few training examples, and highly effective teacher intervention at early stages of learning.

This paper proposes an end-to-end learning from demonstration framework for teaching force-based manipulation tasks to robots. The strengths of this work are manyfold. First, we deal with the problem of learning through force perceptions exclusively. Second, we propose to exploit haptic feedback both as a means for improving teacher demonstrations and as a human–robot interaction tool, establishing a bidirectional communication channel between the teacher and the robot, in contrast to the works using kinesthetic teaching. Third, we address the well-known what to imitate? problem from a different point of view, based on the mutual information between perceptions and actions. Lastly, the teacher's demonstrations are encoded using a Hidden Markov Model, and the robot execution phase is developed by implementing a modified version of Gaussian Mixture Regression that uses implicit temporal information from the probabilistic model, needed when tackling tasks with ambiguous perceptions. Experimental results show that the robot is able to learn and reproduce two different manipulation tasks, with a performance comparable to the teacher's one.

Research in learning from demonstration has focused on transferring movements from humans to robots. However, a need is arising for robots that do not just replicate the task on their own, but that also interact with humans in a safe and natural way to accomplish tasks cooperatively. Robots with variable impedance capabilities opens the door to new challenging applications, where the learning algorithms must be extended by encapsulating force and vision information. In this paper we propose a framework to transfer impedance-based behaviors to a torque-controlled robot by kinesthetic teaching. The proposed model encodes the examples as a task-parameterized statistical dynamical system, where the robot impedance is shaped by estimating virtual stiffness matrices from the set of demonstrations. A collaborative assembly task is used as testbed. The results show that the model can be used to modify the robot impedance along task execution to facilitate the collaboration, by triggering stiff and compliant behaviors in an on-line manner to adapt to the user's actions.

Robot learning from demonstration faces new challenges when applied to tasks in which forces play a key role. Pouring liquid from a bottle into a glass is one such task, where not just a motion with a certain force profile needs to be learned, but the motion is subtly conditioned by the amount of liquid in the bottle. In this paper, the pouring skill is taught to a robot as follows. In a training phase, the human teleoperates the robot using a haptic device, and data from the demonstrations are statistically encoded by a parametric hidden Markov model, which compactly encapsulates the relation between the task parameter (dependent on the bottle weight) and the force-torque traces. Gaussian mixture regression is then used at the reproduction stage for retrieving the suitable robot actions based on the force perceptions. Computational and experimental results show that the robot is able to learn to pour drinks using the proposed framework, outperforming other approaches such as the classical hidden Markov models in that it requires less training, yields more compact encodings and shows better generalization capabilities.

The development of ion-selective electrodes (ISEs) using solid-state transducer materials is of great interest for advanced potentiometric detection systems. At present, conducting polymers are the most used solid-state transducing materials. However, their reliability is strongly related to their chemical stability and the formation of internal water films. Here we report on the use of reduced graphene oxide (RGO) films of different thicknesses as transducer materials in potentiometric all ISEs. First, the transduction mechanism is fully analyzed, revealing that RGO films act as asymmetric capacitors where their electron density is in contact with ions of the electrolyte solution, creating a capacitance due to the constant phase elements present in the system. Second, as a proof of concept, RGO films are used in a calcium ISE showing highly reproducible sensing responses and outstanding increased signal-to-noise ratios with drifts of only 10 mu V/h. These performance parameters are among the best compared to those of other ISE transducer materials so far. With its ease of fabrication and processing into reproducible films of controlled thickness and ease for further tailoring chemical composition and tailoring electrical properties, RGO offers great promise as a reliable high-performance transducer material for solid-state ISE sensors.

A learning framework with a bidirectional communication channel is proposed, where a human performs several demonstrations of a task using a haptic device (providing him/her with force-torque feedback) while a robot captures these executions using only its force-based perceptive system. Our work departs from the usual approaches to learning by demonstration in that the robot has to execute the task blindly, relying only on force-torque perceptions, and, more essential, we address goal-driven manipulation tasks with multiple solution trajectories, whereas most works tackle tasks that can be learned by just finding a generalization at the trajectory level. To cope with these multiple-solution tasks, in our framework demonstrations are represented by means of a Hidden Markov Model (HMM) and the robot reproduction of the task is performed using a modified version of Gaussian Mixture Regression that incorporates temporal information (GMRa) through the forward variable of the HMM. Also, we exploit the haptic device as a teaching and communication tool in a human-robot interaction context, as an alternative to kinesthetic-based teaching systems. Results show that the robot is able to learn a container-emptying task relying only on force-based perceptions and to achieve the goal from several non-trained initial conditions.

We report the formation of a solid-state charge-transfer complex upon simultaneous reduction of a graphene oxide polyaniline (GO-PANT) composite consisting of GO sheets coated by a thin layer of PANT. The reduced R(GO-PANI) material exhibits an unprecedented donor acceptor interaction at the interface between RGO sheets and the thin PANT layer coating. A conceptual explanation is proposed in which RGO plays a dual role as electron acceptor and as large counterion stabilizing an atypical intermediate oxidation state of PANI. Moreover, the donor acceptor interactions are responsible for superior materials characteristics, such as excellent water dispersibility, high environmental (chemical and thermal) degradation stability, and enhanced electric conductivity as high as 2600 S/m. These results may enable further opportunities for the development of novel electroactive materials based on graphene and intrinsically conducting polymers and the fabrication of corresponding flexible electronic devices through traditional solution processing techniques.

In this paper, an investigation of flexible electrodes for photovoltaic textile structures utilizing polymer‐based organic materials is presented. The composite structure consisting of a blend of water dispersible carbon nanotube:polyaniline (CNT:PANI) components with poly(3,4 ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) was applied to be used as the hole collecting electrode in photovoltaic textile applications. Both photovoltaic textiles and conventional solar cells were fabricated by using a blend of poly(3‐hexylthiophene‐2,5‐diyl) (P3HT):(6,6)‐phenyl C61‐butyric acid methyl ester (PCBM). All devices were characterized by measuring current versus voltage characteristics under AM 1.5 conditions. The nanoscale morphology of the photovoltaic structures was investigated using scanning electron microscopy and atomic force microscopy.

Researchers are becoming aware of the importance of other information sources besides visual data in robot learning by demonstration (LbD). Forcebased perceptions are shown to convey very relevant information – missed by visual and position sensors – for learning specific tasks. In this paper, we review some recent works using forces as input data in LbD and Human-Robot interaction (HRI) scenarios, and propose a complete learning framework for teaching force-based manipulation skills to a robot through a haptic device. We suggest to use haptic interfaces not only as a demonstration tool but also as a communication channel between the human and the robot, getting the teacher more involved in the teaching process by experiencing the force signals sensed by the robot. Within the proposed framework, we provide solutions for treating force signals, extracting relevant information about the task, encoding the training data and generalizing to perform successfully under unknown conditions.

Gaussian mixtures-based learning algorithms are suitable strategies for trajectory learning and skill acquisition, in the context of programming by demonstration (PbD). Input streams other than visual information, as used in most applications up to date, reveal themselves as quite useful in trajectory learning experiments where visual sources are not available. In this work we have used force/torque feedback through a haptic device for teaching a teleoperated robot to empty a rigid container. Structure vibrations and container inertia appeared to considerably disrupt the sensing process, so a filtering algorithm had to be devised. Moreover, some input variables seemed much more relevant to the particular task to be learned than others, which lead us to analyze the training data in order to select those relevant features through principal component analysis and a mutual information criterion. Then, a batch version of GMM/GMR [1], [2] was implemented using different training datasets (original, pre-processed data through PCA and MI). Tests where the teacher was instructed to follow a strategy compared to others where he was not lead to useful conclusions that permit devising the new research stages.