Motivated by the vision of integrating mobile robots closer to humans in warehouses, hospitals, manufacturing plants, and the home, we focus on robot navigation in dynamic and spatially constrained environments. Ensuring human safety, comfort, and efficiency in such settings requires that robots are endowed with a model of how humans move around them. Human motion prediction around robots is especially challenging due to the stochasticity of human behavior, differences in user preferences, and data scarcity. In this work, we perform a methodical investigation of the effects of human motion prediction quality on robot navigation performance, as well as human productivity and impressions. We design a scenario involving robot navigation among two human subjects in a constrained workspace and instantiate it in a user study (N=80) involving two different robot platforms, conducted across two sites from different world regions. Key findings include evidence that: 1) the widely adopted average displacement error is not a reliable predictor of robot navigation performance and human impressions; 2) the common assumption of human cooperation breaks down in constrained environments, with users often not reciprocating robot cooperation, and causing performance degradations; 3) more efficient robot navigation often comes at the expense of human efficiency and comfort.

A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation . While the field of social navigation has advanced tremendously in recent years, the fair evaluation of algorithms that tackle social navigation remains hard because it involves not just robotic agents moving in static environments but also dynamic human agents and their perceptions of the appropriateness of robot behavior. In contrast, clear, repeatable, and accessible benchmarks have accelerated progress in fields like computer vision, natural language processing and traditional robot navigation by enabling researchers to fairly compare algorithms, revealing limitations of existing solutions and illuminating promising new directions. We believe the same approach can benefit social navigation. In this article, we pave the road toward common, widely accessible, and repeatable benchmarking criteria to evaluate social robot navigation. Our contributions include (a) a definition of a socially navigating robot as one that respects the principles of safety, comfort, legibility, politeness, social competency, agent understanding, proactivity, and responsiveness to context, (b) guidelines for the use of metrics, development of scenarios, benchmarks, datasets, and simulators to evaluate social navigation, and (c) a design of a social navigation metrics framework to make it easier to compare results from different simulators, robots, and datasets.

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.

Research in joint action focuses on the psychological, neurological, and physical mechanisms by which humans collabo-rate with other agents, and overlaps with several domains related to human-robot interaction. The development of artificial systems that can support or emulate the requisite aspects of joint action could lead to improved human-robot team performance as well as improvements in subjective metrics (e.g., trust). This workshop highlights theoretical and technical considerations about human-robot joint action and real-time adaptation, with a particular focus on socio-motor entrainment, showing how the emulation of psychological mechanisms (e.g., emotion, intention signaling, mirroring) can lead to improved performance. We will invite speakers with backgrounds in robotics, neuroscience and psychol-ogy, as well as speakers with a focus in adjacent works, such as in human-robot coordinated dance, alignment, or synchronization. We will call for papers that utilize the theory of joint-action in an interactive human-robot context. We will also call for position papers on the application of the theory of joint action to robotics, with a heavy focus on psychological mechanisms that could potentially be emulated or adapted to a human-robot context. Participants will have the opportunity to brainstorm considerations and techniques that would be applicable to joint action inspired works through breakout sessions with the aim to lead to new and improved collaborations across fields.

The use of drones in human-populated areas is increasing day by day. Such robots flying in close proximity to humans and potentially interacting with them, as in object handover or delivery, need to carefully plan their navigation considering the presence of humans. We propose a humanaware 3D reactive planner based on stochastic optimization for drone navigation. Besides considering the kinematics constraints of the drone, we propose two criteria to produce socially acceptable trajectories. The first, called discomfort, considers the unease caused to the humans spatially close to fast-moving drones. The second, called visibility, promotes the drone's visibility for humans. We demonstrate the planner's performance and adaptability in various simulated experiments.

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.

We propose a new framework combining decision making and planning in the human-robot co-navigation scenario. This new framework, called HATEB-2, introduces different modalities of planning and shift between them based on the situation at hand. These transitions are controlled by the decision making loop present on top of the planning. We also present the improvements made to human prediction and estimation along with the modifications to a few social constraints from our previous work, that are included in HATEB-2. Finally, several experiments are performed in human-robot co-navigation scenarios and results are presented. One of the modalities of HATEB-2 is used in EU-funded MuMMER [1] project (http://mummer-project.eu/).

Planning frameworks for autonomous vehicles must be robust and computationally efficient for real time realization. At the same time, they should accommodate the unpredictable behavior of the other participants and produce safe trajectories. In this paper, we present a computationally efficient hierarchical planning framework for autonomous vehicles that can generate safe trajectories in complex driving scenarios, which are commonly encountered in urban traffic settings. The first level of the proposed framework constructs a Model Predictive Control(MPC)routine using an efficient difference of convex programmingapproach, that generates smooth and collision-free trajectories. The constraints on curvature and road boundaries are seamlessly integrated into this optimization routine. The second layer is mainly responsible to handle the unpredictable behaviors that are typically exhibited by the other participants of traffic. It is built along the lines of time scaled collision cone(TSCC)which optimize for the velocities along the trajectory to handle such disturbances. We additionally show that our framework maintains optimal balance between temporal and path deviations while executing safe trajectories. To demonstrate the efficacy of the presented framework we validated it in extensive simulations in different driving scenarios like over taking, lane merging and jaywalking among many dynamic and static obstacles.

Stair Climbing is a key functionality desired for robots deployed in Urban Search and Rescue (USAR) scenarios. A novel compliant modular robot was proposed earlier to climb steep and big obstacles. This work extends the functionality of this robot to ascend and descend stairs of dimensions that are also typical of an urban setting. Stair Climbing is realized by equipping the robot's link joints with optimally designed passive spring pairs that resist clockwise and counter clockwise moments generated by the ground during the climbing motion. This 3-module robot is only propelled by wheel actuators. Desirable stair climbing configurations are estimated a-priori and used to obtain the optimal stiffness for springs. Extensive numerical simulation results over different stair configurations are shown. The numerical simulations are corroborated by experimentation using the prototype and its performance is tabulated for different types of surfaces.