SoRTS: Learned Tree Search for Long Horizon Social Robot Navigation

The fast-growing demand for fully autonomous robots in shared spaces calls for the development of trustworthy agents that can safely and seamlessly navigate in crowded environments. Recent models for motion prediction show promise in characterizing social interactions in such environments. Still, adapting them for navigation is challenging as they often suffer from generalization failures. Prompted by this, we propose Social Robot Tree Search (SoRTS), an algorithm for safe robot navigation in social domains. SoRTS aims to augment existing socially aware motion prediction models for long-horizon navigation using Monte Carlo Tree Search. We use social navigation in general aviation as a case study to evaluate our approach and further the research in full-scale aerial autonomy. In doing so, we introduce XPlaneROS, a high-fidelity aerial simulator that enables human-robot interaction. We use XPlaneROS to conduct a first-of-its-kind user study where 26 FAA-certified pilots interact with a human pilot, our algorithm, and its ablation. Our results, supported by statistical evidence, show that SoRTS exhibits a comparable performance to competent human pilots, significantly outperforming its ablation. Finally, we complement these results with a broad set of self-play experiments to showcase our algorithm's performance in scenarios with increasing complexity.Comment: arXiv admin note: substantial text overlap with arXiv:2304.0142

Social-aware drone navigation using social force model

Robot’s navigation is one of the hardest challenges to deal with, because real environments imply highly dynamic objects moving in all directions. The main ideal goal is to conduct a safe navigation within the environment, avoiding obstacles and reaching the final proposed goal. Nowadays, with the last advances in technology, we are able to see robots almost everywhere, and this can lead us to think about the robot’s role in the future, and where we would find them, and it is no exaggerated to say, that practically, flying and land-based robots are going to live together with people, interacting in our houses, streets and shopping centers. Moreover, we will notice their presence, gradually inserted in our human societies, every time doing more human tasks, which in the past years were unthinkable. Therefore, if we think about robots moving or flying around us, we must consider safety, the distance the robot should take to make the human feel comfortable, and the different reactions people would have. The main goal of this work is to accompany people making use of a flying robot. The term social navigation gives us the path to follow when we talk about a social environment. Robots must be able to navigate between humans, giving sense of security to those who are walking close to them. In this work, we present a model called Social Force Model, which states that the human social interaction between persons and objects is inspired in the fluid dynamics de- fined by Newton’s equations, and also, we introduce the extended version which complements the initial method with the human-robot interaction force. In the robotics field, the use of tools for helping the development and the implementation part are crucial. The fast advances in technology allows the international community to have access to cheaper and more compact hardware and software than a decade ago. It is becoming more and more usual to have access to more powerful technology which helps us to run complex algorithms, and because of that, we can run bigger systems in reduced space, making robots more intelligent, more compact and more robust against failures. Our case was not an exception, in the next chapters we will present the procedure we followed to implement the approaches, supported by different simulation tools and software. Because of the nature of the problem we were facing, we made use of Robotic Operating System along with Gazebo, which help us to have a good outlook of how the code will work in real-life experiments. In this work, both real and simulated experiments are presented, in which we expose the interaction conducted by the 3D Aerial Social Force Model, between humans, objects and in this case the AR.Drone, a flying drone property of the Instituto de Robótica e Informática Industrial. We focus on making the drone navigation more socially acceptable by the humans around; the main purpose of the drone is to accompany a person, which we will call the "main" person in this work, who is going to try to navigate side-by-side, with a behavior being dictated with some forces exerted by the environment, and also is going to try to be the more socially close acceptable possible to the remaining humans around. Also, it is presented a comparison between the 3D Aerial Social Force Model and the Artificial Potential Fields method, a well-known method and widely used in robot navigation. We present both methods and the description of the forces each one involves. Along with these two models, there is also another important topic to introduce. As we said, the robot must be able to accompany a pedestrian in his way, and for that reason, the forecasting capacity is an important feature since the robot does not know the final destination of the human to accompany. It is essential to give it the ability to predict the human movements. In this work, we used the differential values between the past position values to know how much is changing through time. This gives us an accurate idea of how the human would behave or which direction he/she would take next. Furthermore, we present a description of the human motion prediction model based on linear regression. The motivation behind the idea of building a Regression Model was the simplicity of the implementation, the robustness and the very accurate results of the approach. The previous main human positions are taken, in order to forecast the new position of the human, the next seconds. This is done with the main purpose of letting the drone know about the direction the human is taking, to move forward beside the human, as if the drone was accompanying him. The optimization for the linear regression model, to find the right weights for our model, was carried out by gradient descent, implementing also de RMSprop variant in order to reach convergence in a faster way. The strategy that was followed to build the prediction model is explained with detail later in this work. The presence of social robots has grown during the past years, many researchers have contributed and many techniques are being used to give them the capacity of interacting safely and effectively with the people, and it is a hot topic which has matured a lot, but still there is many research to be investigated

A Survey on Socially Aware Robot Navigation: Taxonomy and Future Challenges

Socially aware robot navigation is gaining popularity with the increase in delivery and assistive robots. The research is further fueled by a need for socially aware navigation skills in autonomous vehicles to move safely and appropriately in spaces shared with humans. Although most of these are ground robots, drones are also entering the field. In this paper, we present a literature survey of the works on socially aware robot navigation in the past 10 years. We propose four different faceted taxonomies to navigate the literature and examine the field from four different perspectives. Through the taxonomic review, we discuss the current research directions and the extending scope of applications in various domains. Further, we put forward a list of current research opportunities and present a discussion on possible future challenges that are likely to emerge in the field

Human-Machine Interfaces for Service Robotics

L'abstract è presente nell'allegato / the abstract is in the attachmen

What do Collaborations with the Arts Have to Say About Human-Robot Interaction?

This is a collection of papers presented at the workshop What Do Collaborations with the Arts Have to Say About HRI , held at the 2010 Human-Robot Interaction Conference, in Osaka, Japan

Combining motion planning with social reward sources for collaborative human-robot navigation task design

Across the human history, teamwork is one of the main pillars sustaining civilizations and technology development. In consequence, as the world embraces omatization, human-robot collaboration arises naturally as a cornerstone. This applies to a huge spectrum of tasks, most of them involving navigation. As a result, tackling pure collaborative navigation tasks can be a good first foothold for roboticists in this enterprise. In this thesis, we define a useful framework for knowledge representation in human-robot collaborative navigation tasks and propose a first solution to the human-robot collaborative search task. After validating the model, two derived projects tackling its main weakness are introduced: the compilation of a human search dataset and the implementation of a multi-agent planner for human-robot navigatio

Path planning and control of flying robots with account of human’s safety perception

In this dissertation, a framework for planning and control of flying robot with the account of human’s safety perception is presented. The framework enables the flying robot to consider the human’s perceived safety in path planning. First, a data-driven model of the human’s safety perception is estimated from human’s test data using a virtual reality environment. A hidden Markov model (HMM) is considered for estimation of latent variables, as user’s attention, intention, and emotional state. Then, an optimal motion planner generates a trajectory, parameterized in Bernstein polynomials, which minimizes the cost related to the mission objectives while satisfying the constraints on the predicted human’s safety perception. Using Model Predictive Path Integral (MPPI) framework, the algorithm is possible to execute in real-time measuring the human’s spatial position and the changes in the environment. A HMM-based Q-learning is considered for computing the online optimal policy. The HMM-based Q-learning estimates the hidden state of the human in interactions with the robot. The state estimator in the HMM-based Q-learning infers the hidden states of the human based on past observations and actions. The convergence of the HMM-based Q-learning for a partially observable Markov decision process (POMDP) with finite state space is proved using stochastic approximation technique. As future research direction one can consider to use recurrent neural networks to estimate the hidden state in continuous state space. The analysis of the convergence of the HMM-based Q-learning algorithm suggests that the training of the recurrent neural network needs to consider both the state estimation accuracy and the optimality principle