Amit Kumar Pandey

Human-aware robot navigation: A survey

Navigation is a basic skill for autonomous robots. In the last years human-robot interaction has become an important research field that spans all of the robot capabilities including perception, reasoning, learning, manipulation and navigation. For navigation, the presence of humans requires novel approaches that take into account the constraints of human comfort as well as social rules. Besides these constraints, putting robots among humans opens new interaction possibilities for robots, also for navigation tasks, such as robot guides. This paper provides a survey of existing approaches to human-aware navigation and offers a general classification scheme for the presented methods.

Mightability maps: A perceptual level decisional framework for co-operative and competitive human-robot interaction

Interestingly Humans are able to maintain rough estimations of visibility, reachability and other capabilities of not only themselves but of the person they are interacting with. Studies in neuroscience and psychology suggest that from the age of 12–15 months children start to understand the occlusion of others line-of-sight and from the age of 3 years they start to develop the ability, termed as perceived reachability for self and for others. As such capabilities evolve in the children, they start showing intuitive and proactive behavior by perceiving various abilities of the human partner.

A framework towards a socially aware Mobile Robot motion in Human-Centered dynamic environment

For a Mobile Robot to navigate in the Human-Centered environment without imposing alien like impression by its motion, it should be able to reason about various criteria ranging from clearance, environment structure, unknown objects, social conventions, proximity constraints, presence of an individual or group of peoples, etc. Also the robot should neither be over-reactive nor be simple wait and move machine. We have adapted a Voronoi diagram based approach for the analysis of local clearance and environment structure. We also propose to treat human differently from other obstacles for which the robot constructs different sets of regions around human and iteratively converges to a set of points (milestones), using social conventions, human proximity guidelines and clearance constraints to generate and modify its path smoothly. Once equipped with such capabilities, robot is able to do higher-level reasoning for dynamic and selective adaptation of social convention depending upon the environment segment. It also leads the robot to be aware about its own motion behavior.

Towards a Task-Aware Proactive Sociable Robot Based on Multi-state Perspective-Taking

Robots are expected to cooperate with humans in day-to-day interaction. One aspect of such cooperation is behaving proactively. In this paper we will enable our robots, equipped with visuo-spatial perspective-taking capabilities, to behave proactively based on reasoning ‘where’ its human partner might perform a particular task with different effort levels. For this, the robot analyzes the agents’ abilities not only from the current state but also from a set of different states the agent might attain.Depending on the task and the situation, the robot exhibits different types of proactive behaviors, such as, reaching out, suggesting a solution and providing clues by head movement, for two different tasks performed by the human partner: give and make accessible. These proactive behaviors are intended to be informative to reduce confusion of the human partner, to communicate the robot’s ability and intention and to guide the partner for better cooperation.We have validated the behaviors by user studies, which suggest that such proactive behaviors reduce the ‘confusion’ and ‘effort’ of the users. Further, the participants reported the robot to be more ‘supportive and aware’ compared to the situations where the robot was non-proactive.Such proactive behaviors could enrich multi-modal interaction and cooperation capabilities of the robot as well as help in developing more complex socially expected and accepted behaviors in the human centered environment.

Towards shared attention through geometric reasoning for Human Robot Interaction

Human Robot Interaction brings new challenges to the geometric reasoning and space sharing. The robot should not only reason on its own capacities but also consider the actual situation by looking from humans eyes, thus “putting itself to humans perspective”. In humans, the “visual perspective taking” ability begins to appear by 24 months of age and is used to determine if another person can see an object or not. In this paper, we present a geometric reasoning mechanism that employs psychological concepts of “perspective taking” and “mental rotation” in order to reason what the human sees, what the robot sees and where the robot should focus to share humans attention. This geometric reasoning mechanism is demonstrated with HRP-2 humanoid robot in a human-robot face-to-face interaction context.

An interface for interleaved symbolic-geometric planning and backtracking

While symbolic planners work with an abstract representation of the real world, allowing plans to be constructed relatively quickly, geometric planning - although more computationally complex - is essential for building symbolic plans that actually work in the real world. To combine the two types of systems, we present in this paper a meaningful interface, and insights into a methodology for developing interwoven symbolic-geometric domains. We concretely present this “link” between the two approaches with algorithms and data structures that amount to an intermediate layer that coordinates symbolic-geometric planning. Since both planners are capable of “backtracking” at their own levels, we also investigate the issue of how to interleave their backtracking, which we do in the context of the algorithms that form the link. Finally, we present a prototype implementation of the combined system on a PR2 robot.

How to Build a Supervised Autonomous System for Robot-Enhanced Therapy for Children with Autism Spectrum Disorder

Abstract Robot-Assisted Therapy (RAT) has successfully been used to improve social skills in children with autism spectrum disorders (ASD) through remote control of the robot in so-called Wizard of Oz (WoZ) paradigms.However, there is a need to increase the autonomy of the robot both to lighten the burden on human therapists (who have to remain in control and, importantly, supervise the robot) and to provide a consistent therapeutic experience. This paper seeks to provide insight into increasing the autonomy level of social robots in therapy to move beyond WoZ. With the final aim of improved human-human social interaction for the children, this multidisciplinary research seeks to facilitate the use of social robots as tools in clinical situations by addressing the challenge of increasing robot autonomy.We introduce the clinical framework in which the developments are tested, alongside initial data obtained from patients in a first phase of the project using a WoZ set-up mimicking the targeted supervised-autonomy behaviour. We further describe the implemented system architecture capable of providing the robot with supervised autonomy.

The MuMMER Project: Engaging Human-Robot Interaction in Real-World Public Spaces

MuMMER (MultiModal Mall Entertainment Robot) is a four-year, EU-funded project with the overall goal of developing a humanoid robot (SoftBank Robotics’ Pepper robot being the primary robot platform) with the social intelligence to interact autonomously and naturally in the dynamic environments of a public shopping mall, providing an engaging and entertaining experience to the general public. Using co-design methods, we will work together with stakeholders including customers, retailers, and business managers to develop truly engaging robot behaviours. Crucially, our robot will exhibit behaviour that is socially appropriate and engaging by combining speech-based interaction with non-verbal communication and human-aware navigation. To support this behaviour, we will develop and integrate new methods from audiovisual scene processing, social-signal processing, high-level action selection, and human-aware robot navigation. Throughout the project, the robot will be regularly deployed in Ideapark, a large public shopping mall in Finland. This position paper describes the MuMMER project: its needs, the objectives, R&D challenges and our approach. It will serve as reference for the robotics community and stakeholders about this ambitious project, demonstrating how a co-design approach can address some of the barriers and help in building follow-up projects.

Towards multi-state visuo-spatial reasoning based proactive human-robot interaction

Robots are expected to co-operate with humans in day-to-day interaction. One aspect of such co-operation is behaving proactively. In this paper, our robot will exploit the visuo-spatial perspective-taking of the human partner not only from his current state but also from a set of different states he might attain from his current state. Such rich information will help the robot in better predicting ‘where’ the human can perform a particular task and how the robot could support it. We have tested the system on two different robots for the tasks of giving and making an object accessible to the robot by the human partner. Our robots equipped with such multi-state visuo-spatial perspective-taking capabilities show different proactive behaviors depending upon the task and situation, such as reach out proactively and to a correct place, when human has to give an object to the robot. Primary results of user studies show that such proactive behaviors reduce the humans ‘confusion’ as well as ‘the robot’ seems to be more ‘aware’ about the task and the human.

A new approach to combined symbolic-geometric backtracking in the context of human-robot interaction

Bridging the gap between symbolic and geometric planning has received much attention in recent years. An important issue in some of the works that combine the two approaches is finding the right balance between backtracking at the symbolic level versus at the geometric planning level. We present in this work a new approach to interleaved backtracking, where the symbolic planner backtracks to try alternative action branches that naturally map to different geometric solutions. This eliminates the need to “protect” certain symbolic conditions when backtracking at the geometric level, and addresses a completeness issue in our previous approach to interleaved backtracking. We discuss a concrete, non-trivial symbolic-geometric planning example in the context of Human-Robot Interaction, a full implementation of the combined planning technique, and an evaluation of performance as well as the effect of increasing the symbolic-action branching factor.