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Minimally Invasive Procedures (MIPs) emerged as an alternative to more invasive surgical approaches, offering patient benefits such as smaller incisions, less pain, and shorter hospital stay. In one class of MIPs, where natural body lumens or small incisions are used to access deeper anatomical locations, Flexible Surgical and Interventional Robots (FSIRs) such as catheters and endoscopes are widely used. Due to their flexible and compliant nature, FSIRs can be inserted via natural orifices or small incisions, then moved towards hard-to-reach targets to perform interventional tasks. However, existing FSIRs are confronted with challenges in sensing, control, and navigation. These issues stem from the robot's non-linear behavior and the intricate nature of the lumens, where accurately modeling the complex interactions and disturbances proves to be exceptionally difficult. The rapid advances in Machine Learning (ML) have facilitated the widespread adoption of ML techniques in FSIRs. This article provides an overview of these efforts by first introducing a classification of existing ML algorithms, including traditional ML methods and modern Deep Learning (DL) approaches, commonly used in FSIRs. Next, the use of ML algorithms is surveyed per sub-domain, namely for perception, modeling, control, and navigation. Trends, popularity, strengths, and/or limitations of different ML algorithms are analyzed. The different roles that ML plays among tasks are investigated and described. Finally, discussions are conducted on the limitations and the prospects of ML in MIPs., Medical Instruments & Bio-Inspired Technology, Human-Robot Interaction, Learning & Autonomous Control

The development of automated vehicles has the potential to revolutionize transportation, but they are currently unable to ensure a safe and time-efficient driving style. Reliable models predicting human behavior are essential for overcoming this issue. While data-driven models are commonly used to this end, they can be vulnerable in safety-critical edge cases. This has led to an interest in models incorporating cognitive theory, but as such models are commonly developed for explanatory purposes, this approach's effectiveness in behavior prediction has remained largely untested so far. In this article, we investigate the usefulness of the Commotions model - a novel cognitively plausible model incorporating the latest theories of human perception, decision-making, and motor control - for predicting human behavior in gap acceptance scenarios, which entail many important traffic interactions such as lane changes and intersections. We show that this model can compete with or even outperform well-established data-driven prediction models across several naturalistic datasets. These results demonstrate the promise of incorporating cognitive theory in behavior prediction models for automated vehicles., Human-Robot Interaction, Learning & Autonomous Control

Learning from humans allows nonexperts to program robots with ease, lowering the resources required to build complex robotic solutions. Nevertheless, such data-driven approaches often lack the ability to provide guarantees regarding their learned behaviors, which is critical for avoiding failures and/or accidents. In this work, we focus on reaching/point-to-point motions, where robots must always reach their goal, independently of their initial state. This can be achieved by modeling motions as dynamical systems and ensuring that they are globally asymptotically stable. Hence, we introduce a novel Contrastive Learning loss for training deep neural networks (DNN) that, when used together with an Imitation Learning loss, enforces the aforementioned stability in the learned motions. Differently from previous work, our method does not restrict the structure of its function approximator, enabling its use with arbitrary DNNs and allowing it to learn complex motions with high accuracy. We validate it using datasets and a real robot. In the former case, motions are two- and four-dimensional, modeled as first- and second-order dynamical systems. In the latter, motions are three, four, and six-dimensional, of first and second order, and are used to control a 7-DoF robot manipulator in its end effector space and joint space., Learning & Autonomous Control

Balancing safety and efficiency when planning in crowded scenarios with uncertain dynamics is challenging where it is imperative to accomplish the robot's mission without incurring any safety violations. Typically, chance constraints are incorporated into the planning problem to provide probabilistic safety guarantees by imposing an upper bound on the collision probability of the planned trajectory. Yet, this results in an overly conservative behavior on the grounds that the gap between the obtained risk and the specified upper limit is not explicitly restricted. To address this issue, we propose a real-time capable approach to quantify the risk associated with planned trajectories obtained from multiple probabilistic planners, running in parallel, with different upper bounds of the acceptable risk level. Based on the evaluated risk, the least conservative plan is selected provided that its associated risk is below a specified threshold. In such a way, the proposed approach provides probabilistic safety guarantees by attaining a closer bound to the specified risk, while being applicable to generic uncertainties of moving obstacles. We demonstrate the efficiency of our proposed approach, by improving the performance of a state-of-the-art probabilistic planner, in simulations and experiments using a mobile robot in an environment shared with humans., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control

Being one of the oldest en most frequently performed invasive procedures; the lack of scientific progress of tooth removal procedures is impressive. This has most likely to do with technical limitations in measuring different aspects of these keyhole procedures. The goal of this study is to accurately capture the full range of motions during tooth removal as well as angular velocities in clinically relevant directions. An ex vivo measuring setup was designed consisting of, amongst others, a compliant robot arm. To match clinical conditions as closely as possible, fresh-frozen cadavers were used as well as regular dental forceps mounted on the robot’s end-effector. Data on 110 successful tooth removal experiments are presented in a descriptive manner. Rotation around the longitudinal axis of the tooth seems to be most dominant both in range of motion as in angular velocity. Buccopalatal and buccolingual movements are more pronounced in the dorsal region of both upper and lower jaw. This study quantifies an order of magnitude regarding ranges of motion and angular velocities in tooth removal procedures. Improved understanding of these complex procedures could aid in the development of evidence-based educational material., Learning & Autonomous Control

Autonomous vehicles currently suffer from a time-inefficient driving style caused by uncertainty about human behavior in traffic interactions. Accurate and reliable prediction models enabling more efficient trajectory planning could make autonomous vehicles more assertive in such interactions. However, the evaluation of such models is commonly oversimplistic, ignoring the asymmetric importance of prediction errors and the heterogeneity of the datasets used for testing. We examine the potential of recasting interactions between vehicles as gap acceptance scenarios and evaluating models in this structured environment. To that end, we develop a framework aiming to facilitate the evaluation of any model, by any metric, and in any scenario. We then apply this framework to state-of-the-art prediction models, which all show themselves to be unreliable in the most safety-critical situations., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Human-Robot Interaction, Learning & Autonomous Control

Humans often demonstrate diverse behaviors due to their personal preferences, for instance, related to their individual execution style or personal margin for safety. In this paper, we consider the problem of integrating both path and velocity preferences into trajectory planning for robotic manipulators. We first learn reward functions that represent the user path and velocity preferences from kinesthetic demonstration. We then optimize the trajectory in two steps, first the path and then the velocity, to produce trajectories that adhere to both task requirements and user preferences. We design a set of parameterized features that capture the fundamental preferences in a pick-and-place type of object transportation task, both in the shape and timing of the motion. We demonstrate that our method is capable of generalizing such preferences to new scenarios. We implement our algorithm on a Franka Emika 7-DoF robot arm and validate the functionality and flexibility of our approach in a user study. The results show that non-expert users are able to teach the robot their preferences with just a few iterations of feedback., Biomechatronics & Human-Machine Control, Human-Robot Interaction, Learning & Autonomous Control

In order to deploy robots that could be adapted by non-expert users, interactive imitation learning (IIL) methods must be flexible regarding the interaction preferences of the teacher and avoid assumptions of perfect teachers (oracles), while considering they make mistakes influenced by diverse human factors. In this work, we propose an IIL method that improves the human–robot interaction for non-expert and imperfect teachers in two directions. First, uncertainty estimation is included to endow the agents with a lack of knowledge awareness (epistemic uncertainty) and demonstration ambiguity awareness (aleatoric uncertainty), such that the robot can request human input when it is deemed more necessary. Second, the proposed method enables the teachers to train with the flexibility of using corrective demonstrations, evaluative reinforcements, and implicit positive feedback. The experimental results show an improvement in learning convergence with respect to other learning methods when the agent learns from highly ambiguous teachers. Additionally, in a user study, it was found that the components of the proposed method improve the teaching experience and the data efficiency of the learning process., Learning & Autonomous Control

In recent years, imitation learning (IL) has been widely used in industry as the core of autonomous vehicle (AV) planning modules. However, previous IL works show sample inefficiency and low generalisation in safety-critical scenarios, on which they are rarely tested. As a result, IL planners can reach a performance plateau where adding more training data ceases to improve the learnt policy. First, our work presents an IL model using the spline coefficient parameterisation and offline expert queries to enhance safety and training efficiency. Then, we expose the weakness of the learnt IL policy by synthetically generating critical scenarios through optimisation of parameters of the driver's risk field (DRF), a parametric human driving behaviour model implemented in a multi-agent traffic simulator based on the Lyft Prediction Dataset. To continuously improve the learnt policy, we retrain the IL model with augmented data. Thanks to the expressivity and interpretability of the DRF, the desired driving behaviours can be encoded and aggregated to the original training data. Our work constitutes a full development cycle that can efficiently and continuously improve the learnt IL policies in closed-loop. Finally, we show that our IL planner developed with less training resource still has superior performance compared to the previous state-of-the-art., Learning & Autonomous Control

Intelligent manufacturing is becoming increasingly important due to the growing demand for maximizing productivity and flexibility while minimizing waste and lead times. This work investigates automated secondary robotic food packaging solutions that transfer food products from the conveyor belt into containers. A major problem in these solutions is varying product supply which can cause drastic productivity drops. Conventional rule-based approaches, used to address this issue, are often inadequate, leading to violation of the industry's requirements. Reinforcement learning, on the other hand, has the potential of solving this problem by learning responsive and predictive policy, based on experience. However, it is challenging to utilize it in highly complex control schemes. In this paper, we propose a reinforcement learning framework, designed to optimize the conveyor belt speed while minimizing interference with the rest of the control system. When tested on real-world data, the framework exceeds the performance requirements (99.8% packed products) and maintains quality (100% filled boxes). Compared to the existing solution, our proposed framework improves productivity, has smoother control, and reduces computation time., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control

This work investigates how the intricate task of a continuous pick & place (P&P) motion may be learned from humans based on demonstrations and corrections. Due to the complexity of the task, these demonstrations are often slow and even slightly flawed, particularly at moments when multiple aspects (i.e., end-effector movement, orientation, and gripper width) have to be demonstrated at once. Rather than training a person to give better demonstrations, non-expert users are provided with the ability to interactively modify the dynamics of their initial demonstration through teleoperated corrective feedback. This in turn allows them to teach motions outside of their own physical capabilities. In the end, the goal is to obtain a faster but reliable execution of the task. The presented framework learns the desired movement dynamics based on the current Cartesian position with Gaussian Processes (GPs), resulting in a reactive, time-invariant policy. Using GPs also allows online interactive corrections and active disturbance rejection through epistemic uncertainty minimization. The experimental evaluation of the framework is carried out on a Franka-Emika Panda. Tests were performed to determine i) the framework's effectiveness in successfully learning how to quickly pick & place an object, ii) ease of policy correction to environmental changes (i.e., different object sizes and mass), and iii) the framework's usability for non-expert users., Learning & Autonomous Control

In order to make the coexistence between humans and robots a reality, we must understand how they may cooperate more effectively. Modern robots, empowered with reliable controls and advanced machine learning reasoning can face this challenge. In this article, we presented a Disagreement- Aware Variable Impedance (DAVI) Controller, where the robot stiffness is regulated as a function of the perceived disagreement with the human cooperator. We tested the algorithm on a 7 DoF Franka Emika Panda robot performing the learning of a pick&place task with continuous adaptation of the goal location and the via-points with human interactive corrections, triggered by our proposed approach. A validation study was conducted with 5 users in order to understand the reliability of the method., Paper presented at the workshop "Shared Autonomy in Physical Human-Robot Interaction: Adaptability and Trust" 23 May 2022, Learning & Autonomous Control

Existing Deep Learning (DL) frameworks typically do not provide ready-to-use solutions for robotics, where very specific learning, reasoning, and embodiment problems exist. Their relatively steep learning curve and the different methodologies employed by DL compared to traditional approaches, along with the high complexity of DL models, which often leads to the need of employing specialized hardware accelerators, further increase the effort and cost needed to employ DL models in robotics. Also, most of the existing DL methods follow a static inference paradigm, as inherited by the traditional computer vision pipelines, ignoring active perception, which can be employed to actively interact with the environment in order to increase perception accuracy. In this paper, we present the Open Deep Learning Toolkit for Robotics (OpenDR). OpenDR aims at developing an open, non-proprietary, efficient, and modular toolkit that can be easily used by robotics companies and research institutions to efficiently develop and deploy AI and cognition technologies to robotics applications, providing a solid step towards addressing the aforementioned challenges. We also detail the design choices, along with an abstract interface that was created to overcome these challenges. This interface can describe various robotic tasks, spanning beyond traditional DL cognition and inference, as known by existing frameworks, incorporating openness, homogeneity and robotics-oriented perception e.g., through active perception, as its core design principles., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control, Joining and Mechanical Behavior

Designing controllers for complex robots such as humanoids is not an easy task. Often, researchers hand-tune controllers, but this is a time-consuming approach that yields a single controller which cannot generalize well to varied tasks. This work presents a method which uses the NSGA-II multi-objective optimization algorithm with various training trajectories to output a diverse Pareto set of well-functioning controller weights and gains. The best of these are shown to also work well on the real Talos robot. The learned Pareto front is then used in a Bayesian optimization (BO) algorithm both as a search space and as a source of prior information in the initial mean estimate. This combined learning approach, leveraging the two optimization methods together, finds a suitable parameter set for a new trajectory within 20 trials and outperforms both BO in the continuous parameter search space and random search along the precomputed Pareto front. The few trials required for this formulation of BO suggest that it could feasibly be applied on the physical robot using a Pareto front generated in simulation., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control

The need for a training modality for tooth extraction procedures is increasing, as dental students do not feel properly trained. In this study, a prototype of a training setup is designed, in which extraction procedures can be performed on jaw models and cadaveric jaws. The prototype was designed in a way that it can give real-time feedback on the applied forces in all three dimensions (buccal/lingual, mesial/distal, and apical/coronal), torques, and angular velocity. To evaluate the prototype, a series of experimental extractions on epoxy models, conserved jaws, and fresh frozen jaws were performed. Extraction duration (s), angular velocity (degrees/s), average force (N), average torque (Nm), linear impulse (Ns), and angular impulse (N ms) were shown in real-time to the user and used to evaluate the prototype. In total, 342 (92.9 % ) successful extractions were performed using the prototype (n= 113 epoxy factory-made, n=187 epoxy re-used, n=17 conserved, n=25 fresh frozen). No significant differences were found between the conserved and the fresh frozen jaws. The fresh frozen extraction duration, linear impulse, and angular impulse differed significantly from the corresponding values obtained for the epoxy models. Extractions were successfully performed, and the applied forces, torques, and angular velocity were recorded and shown as real-time feedback using the prototype of the dental extraction trainer. The feedback of the prototype is considered reliable., Support Biomechanical Engineering, Learning & Autonomous Control, Medical Instruments & Bio-Inspired Technology

Surprisingly little is known about tooth removal procedures. This might be due to the difficulty of gaining reliable data on these procedures. To improve our understanding of these procedures, machine learning techniques were used to design a multiclass classification model of tooth removal based on force, torque, and movement data recorded during tooth removal. A measurement setup consisting of, among others, robot technology was used to gather high-quality data on forces, torques, and movement in clinically relevant dimensions. Fresh-frozen cadavers were used to match the clinical situation as closely as possible. Clinically interpretable variables or “features” were engineered and feature selection took place to process the data. A Gaussian naive Bayes model was trained to classify tooth removal procedures. Data of 110 successful tooth removal experiments were available to train the model. Out of 75 clinically designed features, 33 were selected for the classification model. The overall accuracy of the classification model in 4 random subsamples of data was 86% in the training set and 54% in the test set. In 95% and 88%, respectively, the model correctly classified the (upper or lower) jaw and either the right class or a class of neighboring teeth. This article discusses the design and performance of a multiclass classification model for tooth removal. Despite the relatively small data set, the quality of the data was sufficient to develop a first model with reasonable performance. The results of the feature engineering, selection process, and the classification model itself can be considered a strong first step toward a better understanding of these complex procedures. It has the potential to aid in the development of evidence-based educational material and clinical guidelines in the near future., Learning & Autonomous Control

The successful integration of autonomous robots in real-world environments strongly depends on their ability to reason from context and take socially acceptable actions. Current autonomous navigation systems mainly rely on geometric information and hard-coded rules to induce safe and socially compliant behaviors. Yet, in unstructured urban scenarios these approaches can become costly and suboptimal. In this paper, we introduce a motion planning framework consisting of two components: a data-driven policy that uses visual inputs and human feedback to generate socially compliant driving behaviors (encoded by high-level decision variables), and a local trajectory optimization method that executes these behaviors (ensuring safety). In particular, we employ Interactive Imitation Learning to jointly train the policy with the local planner, a Model Predictive Controller (MPC), which results in safe and human-like driving behaviors. Our approach is validated in realistic simulated urban scenarios. Qualitative results show the similarity of the learned behaviors with human driving. Furthermore, navigation performance is substantially improved in terms of safety, i.e., number of collisions, as compared to prior trajectory optimization frameworks, and in terms of data-efficiency as compared to prior learning-based frameworks, broadening the operational domain of MPC to more realistic autonomous driving scenarios., Learning & Autonomous Control

Deformable object manipulation (DOM) is an emerging research problem in robotics. The ability to manipulate deformable objects endows robots with higher autonomy and promises new applications in the industrial, services, and health-care sectors. However, compared to rigid object manipulation, the manipulation of deformable objects is considerably more complex and is still an open research problem. Addressing DOM challenges demands breakthroughs in almost all aspects of robotics: hardware design, sensing, (deformation) modeling, planning, and control. In this article, we review recent advances and highlight the main challenges when considering deformation in each subfield. A particular focus of our article lies in the discussions of these challenges and proposing future directions of research., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control

Moving away from repetitive tasks, robots nowadays demand versatile skills that adapt to different situations. Task-parameterized learning improves the generalization of motion policies by encoding relevant contextual information in the task parameters, hence enabling flexible task executions. However, training such a policy often requires collecting multiple demonstrations in different situations. To comprehensively create different situations is non-trivial thus renders the method less applicable to real-world problems. Therefore, training with fewer demonstrations/situations is desirable. This paper presents a novel concept to augment the original training dataset with synthetic data for policy improvements, thus allows learning task-parameterized skills with few demonstrations., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control

Aleatoric uncertainty estimation, based on the observed training data, is applied for the detection of conflicts in a demonstration data set. The particular focus of this paper is the resolution of conflicting data resulting from scenarios with equivalent action choices, such as obstacle avoidance, path planning or multiple joint configurations. In terms of the estimated uncertainty, the proposed algorithm aims to decrease this otherwise irreducible value through direct alteration of the accrued data set and to provide data that a policy-learning neural network is able to fit appropriately. The proposed algorithm was validated with real robot scenarios while learning from inconsistent demonstrations, where the resulting policies consistently achieved their prescribed objectives. A video showing our method and experiments can be found at: https://youtu.be/oGYnzlW9Ncw., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control

Deep neural networks designed for vision tasks are often prone to failure when they encounter environmental conditions not covered by the training data. Single-modal strategies are insufficient when the sensor fails to acquire information due to malfunction or its design limitations. Multi-sensor configurations are known to provide redundancy, increase reliability, and are crucial in achieving robustness against asymmetric sensor failures. To address the issue of changing lighting conditions and asymmetric sensor degradation in object detection, we develop a multi-modal 2D object detector, and propose deterministic and stochastic sensor-aware feature fusion strategies. The proposed fusion mechanisms are driven by the estimated sensor measurement reliability values/weights. Reliable object detection in harsh lighting conditions is essential for applications such as self-driving vehicles and human-robot interaction. We also propose a new 'r-blended' hybrid depth modality for RGB-D sensors. Through extensive experimentation, we show that the proposed strategies outperform the existing state-of-the-art methods on the FLIR-Thermal dataset, and obtain promising results on the SUNRGB-D dataset. We additionally record a new RGB-Infra indoor dataset, namely L515-Indoors, and demonstrate that the proposed object detection methodologies are highly effective for a variety of lighting conditions., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control

Objectives: To provide dental practitioners and researchers with a comprehensive and transparent evidence-based overview of the characteristics of literature regarding initiatives of robot technology in dentistry. Data: All articles in which robot technology in dentistry is described, except for non-scientific articles and articles containing secondary data (reviews). Amongst others, the following data were extracted: type of study, level of technological readiness, authors’ professional background and the subject of interaction with the robot. Sources: Bibliographic databases PubMed, Embase, and Scopus were surveyed. A reference search was conducted. The search timeline was between January 1985 and October 2020. Study selection: A total of 911 articles were screened on title and abstract of which 161 deemed eligible for inclusion. Another 71 articles were excluded mainly because of unavailability of full texts or the sole use of secondary data (reviews). Four articles were included after hand searching the reference lists. In total, 94 articles were included for analysis. Conclusions: Since 2013 an average of six articles per year concern robot initiatives in dentistry, mostly originating from East Asia (57%). The vast majority of research was categorized as either basic theoretical or basic applied research (80%). Technology readiness levels did not reach higher than three (proof of concept) in 55% of all articles. In 84%, the first author of the included articles had a technical background and in 36%, none of the authors had a dental or medical background. The overall quality of literature, especially in terms of clinical validation, should be considered as low., Learning & Autonomous Control

Objectives: To provide dental practitioners and researchers with a comprehensive and transparent evidence-based overview of physical robot initiatives in all fields of dentistry. Data: Articles published since 1985 concerning primary data on physical robot technology in dentistry were selected. Characteristics of the papers were extracted such as the respective field of dentistry, year of publication as well as a description of its usage. Sources: Bibliographic databases PubMed, Embase, and Scopus were searched. A hand search through reference lists of all included articles was performed. Study selection: The search timeline was between January 1985 and October 2020. All types of scientific literature in all languages were included concerning fields of dentistry ranging from student training to implantology. Robot technology solely for the purpose of research and maxillofacial surgery were excluded. In total, 94 articles were included in this systematic review. Conclusions: This study provides a systematic overview of initiatives using robot technology in dentistry since its very beginning. While there were many interesting robot initiatives reported, the overall quality of the literature, in terms of clinical validation, is low. Scientific evidence regarding the benefits, results and cost-efficiency of commercially available robotic solutions in dentistry is lacking. The rise in availability of open source control systems, compliant robot systems and the design of dentistry-specific robot technology might facilitate the process of technological development in the near future. The authors are confident that robotics will provide useful solutions in the future but, strongly, encourage an evidence-based approach when adapting to new (robot) technology., Learning & Autonomous Control

Teaching a robot how to navigate in a new environment only from the sensor input in an end-to-end fashion is still an open challenge with much attention from industry and academia. This paper proposes an algorithm with the name 'Learning Interactively to Resolve Ambiguity' (LIRA) that tackles the problem of sensor policy fusion extending state- of-the-art methods by employing ambiguity awareness in the decision-making and solving it using active and interactive querying of the human expert. LIRA, in fact, employs Gaussian Processes for the estimation of the policy's confidence and investigates the ambiguity due to the disagreement between the single sensor policies on the desired action to take. LIRA aims to make the teaching of new policies easier, learning from human demonstrations and correction.The experiments show that LIRA can be used for learning a sensor-fused policy from scratch or also leveraging the knowledge of existing single sensor policies. The experiments focus on the estimation of the human interventions required for teaching a successful navigation policy., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control