It is essential for a collaborative robot to consider the Theory of Mind (ToM) when interacting with humans. Indeed, performing an action in the absence of another agent may create false beliefs like in the well-known Sally & Anne Task [1]. The robot should be able to detect, react to, and even anticipate false beliefs of other agents with a detrimental impact on the task to achieve. Currently, ToM is mainly used to control the task execution and resolve in a reactive way the detrimental false beliefs. Some works introduce ToM at the planning level by considering distinct beliefs, and we are in this context. This work proposes an extension of an existing human-aware task planner and effectively allows the robot to anticipate a false human belief ensuring a smooth collaboration through an implicitly coordinated plan. First, we propose to capture the observability properties of the environment in the state description using two observability types and the notion of co-presence. They allow us to maintain distinct agent beliefs by reasoning directly on what agents can observe through specifically modeled Situation Assessment processes, instead of reasoning of action effects. Then, thanks to the better estimated human beliefs, we can predict if a false belief with adverse impact will occur. If that is the case then, first, the robot’s plan can be to communicate minimally and proactively. Second, if this false belief is due to a non-observed robot action, the robot’s plan can be to postpone this action until it can be observed by the human, avoiding the creation of the false belief. We implemented our new conceptual approach, discuss its effectiveness qualitatively, and show experimental results on three novel domains.

Human-aware robot navigation planning enables robots to traverse human-occupied spaces socially. However, evaluating and benchmarking the ‘human awareness’ of such navigation schemes is challenging. With the growing necessity and research interest in the field, there is a need to define metrics to quantify and benchmark such qualities. In this regard, this paper proposes a set of metrics by looking at the problem from a new perspective. These proposals are made by inspecting the robot’s navigation from the viewpoint of a human experiencing it and then defining proxies for the perceived human feelings. Analyses of some commonly occurring human-robot navigation scenarios using these metrics show their capability in benchmarking and differentiating human-aware robot navigation from standard robot navigation.

Experimenting, testing, and debugging robot social navigation systems is a challenging task. While simulation is generally well suited for a first level of debugging and evaluation of robotics controllers and planners, the social navigation field lacks satisfactory simulators of humans which act, react and interact rationally and naturally. To facilitate the development of human-aware navigation systems, we propose a system to simulate an autonomous human agent that is both reactive and rational, specifically designed to act and interact with a robot for navigation problems and potential conflicts. Besides, it also provides some metrics to partially evaluate such interactions and data logs for further analysis. We show the limitations of over-used reactive-only approaches. Then, thanks to two different human-aware navigation planners, we show how our system can help answer the lack of intelligent human avatars for tuning and debugging social navigation systems before their final evaluation with real humans.

The variety and complexity of tasks autonomous robots can tackle is constantly increasing, yet we seldom see robots collaborating with humans. Indeed, humans are either requested for punctual help or are given the lead on the whole task. We propose a human-aware task planning approach allowing the robot to plan for a task while also considering and emulating the human decision, action, and reaction processes. Our approach, named Human-Aware Task Planner with Emulation of Human Decisions and Actions (HATP/EHDA), is based on the exploration of multiple hierarchical tasks networks albeit differently whether the agent is considered to be controllable (the robot) or uncontrollable (the human). We present the rationale of our approach along with a formalization and show its potential on an illustrative example.

Current approaches in human-aware or social robot navigation address the humans that are visible to the robot. However, it is also important to address the possible emergences of humans to avoid shocks or surprises to humans and erratic behavior of the robot planner. In this paper, we propose a novel approach to detect and address these human emergences called ‘invisible humans’. We determine the places from which a human, currently not visible to the robot, can appear suddenly and then adapt the path and speed of the robot with the anticipation of potential collisions. This is done while still considering and adapting humans present in the robot's field of view. We also show how this detection can be exploited to identify and address the doorways or narrow passages. Finally, the effectiveness of the proposed methodology is shown through several simulated and real-world experiments.

As more robots are being deployed into human environments, a human-aware navigation planner needs to handle multiple contexts that occur in indoor and outdoor environments. In this paper, we propose a tunable human-aware robot navigation planner that can handle a variety of human-robot contexts. We present the architecture of the system and discuss the features along with some implementation details. Then we present a detailed analysis of various simulated human-robot contexts using the proposed planner. Further, we show that our system performs better when compared with an exiting human-aware planner in various contexts. Finally, we show the results in a real-world scenario after deploying our system on a real robot.