Mobile robots are being introduced to industrial workplaces in roles that require copresence with humans. To develop effective robots that do not negatively impact humans, including their subjective experience and ability to get their work done, we must understand humans’ needs for working near these robots. In this paper, we examine what human workers need from copresent robots’ motion during work at a large warehouse. To do so, report and synthesize findings about robot motion from across five studies (e.g., focus group, observation, experiment). Results indicate that workers were most focused on robot movement, including consistency, distance, prioritizing people, and indicating when it sensed people. Researchers and practitioners can use these findings to prioritize what aspects of mobile robots to develop to improve human worker experiences around robots and team efficiency.

Mobile robots struggle to integrate seamlessly in crowded environments such as pedestrian scenes, often disrupting human activity. One obstacle preventing their smooth integration is our limited understanding of how humans may perceive and react to robot motion. Motivated by recent studies highlighting the benefits of intent-expressive motion for robots operating close to humans, we describe Social Momentum (SM), a planning framework for legible robot motion generation in multiagent domains. We investigate the properties of motion generated by SM via two large-scale user studies: an online, video-based study ( N = 180) focusing on the legibility of motion produced by SM and a lab study ( N = 105) focusing on the perceptions of users navigating next to a robot running SM in a crowded space. Through statistical and thematic analyses of collected data, we present evidence suggesting that (a) motion generated by SM enables quick inference of the robot’s navigation strategy; (b) humans navigating close to a robot running SM follow comfortable, low-acceleration paths; and (c) robot motion generated by SM is positively perceived and indistinguishable from a teleoperated baseline. Through the discussion of experimental insights and lessons learned, this article aspires to inform future algorithmic and experimental design for social robot navigation.

Robot navigation in human spaces today largely relies on the construction of precise geometric maps and a global motion plan. In this work, we navigate with only local sensing by using available signage - as designed for humans - in human-made environments such as airports. We propose a formalization of "signage" and define 4 levels of signage that we call complete, fully-specified, consistent and valid. The signage formalization can be used on many space skeletonizations, but we specifically provide an approach for navigation on the medial axis. We prove that we can achieve global completeness guarantees without requiring a global map to plan. We validate with two sets of experiments: (1) with real-world airports and their real signs and (2) real New York City neighborhoods. In (1) we show we can use real-world airport signage to improve on a simple random-walk approach, and we explore augmenting signage to further explore signs' impact on trajectory length. In (2), we navigate in varied sized subsets of New York City to show that, since we only use local sensing, our approach scales linearly with trajectory length rather than freespace area.

Humans expect their collaborators to look beyond the explicit interpretation of their words. Implicature is a common form of implicit communication that arises in natural language discourse when an utterance leverages context to imply information beyond what the words literally convey. Whereas computational methods have been proposed for interpreting and using different forms of implicature, its role in human and artificial agent collaboration has not yet been explored in a concrete domain. The results of this paper provide insights to how artificial agents should be structured to facilitate natural and efficient communication of actionable information with humans. We investigated implicature by implementing two strategies for playing Hanabi, a cooperative card game that relies heavily on communication of actionable implicit information to achieve a shared goal. In a user study with 904 completed games and 246 completed surveys, human players randomly paired with an implicature AI are 71% more likely to think their partner is human than players paired with a non-implicature AI. These teams demonstrated game performance similar to other state of the art approaches.

State-of-the-art social robot navigation algorithms often lack a thorough experimental validation in human environments: simulated evaluations are often conducted under unrealistically strong assumptions that prohibit deployment in real world environments; experimental demonstrations that are limited in sample size do not provide adequate evidence regarding the user experience and the robot behavior; field studies may suffer from the noise imposed by uncontrollable factors from the environment; controlled lab experiments often fail to properly enforce challenging interaction settings. This paper contributes a first step towards addressing the outlined gaps in the literature. We present an original experiment, designed to test the implicit interaction between a mobile robot and a group of navigating human participants, under challenging settings in a controlled lab environment. We conducted a large-scale, within-subjects design study with 105 participants, exposed to three different conditions, corresponding to three distinct navigation strategies, executed by a telepresence robot (two autonomous, one teleoperated). We analyzed observed human and robot trajectories, under close interaction settings and participants' impressions regarding the robot's behavior. Key findings, extracted from a comparative statistical analysis include: (1) evidence that human acceleration is lower when navigating around an autonomous robot compared to a teleoperated one; (2) the lack of evidence to support the conventional expectation that teleoperation would be humans' preferred strategy. To the best of our knowledge, our study is unique in terms of goals, settings, thoroughness of evaluation and sample size.

Intent-expressive robot motion has been shown to result in increased efficiency and reduced planning efforts for copresent humans. Existing frameworks for generating intent-expressive robot behaviors have typically focused on applications in static or structured environments. Under such settings, emphasis is placed towards communicating the robot»s intended final configuration to other agents. However, in dynamic, unstructured and multi-agent domains, such as pedestrian environments, knowledge of the robot»s final configuration is not sufficiently informative as it completely ignores the complex dynamics of interaction among agents. To address this problem, we design a planning framework that aims at generating motion that clearly communicates an agent»s intended collision avoidance strategy rather than its destination. Our framework estimates the most likely intended avoidance protocols of others based on their past behaviors, superimposes them, and generates an expressive and socially compliant robot action that reinforces the expectations of others regarding these avoidance protocols. This action facilitates inference and decision making for everyone, as illustrated in the simplified topological pattern of agents» trajectories. Extensive simulations demonstrate that our framework consistently achieves significantly lower topological complexity, compared against common benchmark approaches in multi-agent collision avoidance. The significance of this result for real world applications is demonstrated by a user study that reveals statistical evidence suggesting that multi-agent trajectories of lower topological complexity tend to facilitate inference for observers.

Robots must be cognizant of how their actions will be interpreted in context. Actions performed in the context of a joint activity comprise two aspects: functional and communicative. The functional component achieves the goal of the action, whereas its communicative component, when present, expresses some information to the actor's partners in the joint activity. The interpretation of such communication requires leveraging information that is public to all participants, known as common ground. Much of human communication is performed through this implicit mechanism, and humans cannot help but infer some meaning -- whether or not it was intended by the actor -- from most actions. We present a framework for robots to utilize this communicative channel on top of normal functional actions to work more effectively with human partners. We consider the role of the actor and the observer, both individually and jointly, in implicit communication, as well as the effects of timing. We also show how the framework maps onto various modes of action, including natural language and motion. We consider these modes of action in various human-robot interaction domains, including social navigation and collaborative assembly.

We present a novel, data-driven framework for planning socially competent robot behaviors in crowded environments. The core of our approach is a topological model of collective navigation behaviors, based on braid groups. This model constitutes the basis for the design of a human-inspired probabilistic inference mechanism that predicts the topology of multiple agents' future trajectories, given observations of the context. We derive an approximation of this mechanism by employing a neural network learning architecture on synthetic data of collective navigation behaviors. Our planner makes use of this mechanism as a tool for interpreting the context and understanding what future behaviors are in compliance with it. The planning agent makes use of this understanding to determine a personal action that contributes to the context in the most clear way possible, while ensuring progress to its destination. Our simulations provide evidence that our planning framework results in socially competent navigation behaviors not only for the planning agent, but also for interacting naive agents. Performance benefits include (1) early conflict resolutions and (2) faster uncertainty decrease for the other agents in the scene.

A key assumption that drives much of HRI research is that human robot collaboration can be improved by advancing a robot's capabilities. We argue that this assumption has potentially negative implications, as increasing social capabilities in robots can produce an expectations gap where humans develop unrealistically high expectations of social robots due to generalization from human mental models. By conducting two studies with 674 participants, we examine how people develop and adjust mental models of robots. We find that both a robot's physical appearance and its behavior influence how we form these models. This suggests it is possible for a robot to unintentionally manipulate a human into building an inaccurate mental model of its overall abilities simply by displaying a few capabilities that humans possess, such as speaking and turn-taking. We conclude that this expectations gap, if not corrected for, could ironically result in less effective collaborations as robot capabilities improve.

This paper presents a formalism that exploits deformability during manipulation of soft objects by robot teams. A hybrid centralized/distributed approach restricts centralized planning to high-level global guidance of the object for consensus. Low-level control is thus delegated to the individual manipulator robots, which retain manipulation and collision avoidance guarantees by passing forces to one another through the object. A distributed receding horizon planner provides local control, formulated as a convex optimization problem in velocity space and incorporating constraints for both collision avoidance and shape maintenance. We demonstrate teams of mobile manipulators autonomously carrying various deformable objects.

We present an approach to position k servers (e.g. mobile robots) to provide a service to n independently moving clients; for example, in mobile ad-hoc networking applications where inter-agent distances need to be minimized, connectivity constraints exist between servers, and no a priori knowledge of the clients' motion can be assumed. Our primary contribution is an algorithm to compute and maintain a small representative set, called a kinematic coreset, of the n moving clients.We prove that, in any given moment, the maximum distance between the clients and any set of k servers is approximated by the coreset up to a factor of (1 ± ε), where ε > 0 is an arbitrarily small constant. We prove that both the size of our coreset and its update time is polynomial in k log(n)/ε. Although our optimization problem is NP-hard (i.e., takes time exponential in the number of servers to solve), solving it on the small coreset instead of the original clients results in a tractable controller. The approach is validated in a small scale hardware experiment using robot servers and human clients, and in a large scale numerical simulation using thousands of clients.

Highly articulated robot locomotion systems, such as snake robots, present special motion planning challenges. They possess many degrees of freedom, and therefore are modeled by a high dimensional configuration space which must be searched to plan a path. Kinematic and dynamic constraints further complicate the selection of effective controls. Finally, snake robots often have multiple modes of interaction with the terrain as contacts are made and broken, leading to complex and imperfect motion models. We believe that the space of useful controls that provides desirable motions, however, is much smaller. Useful net motions for such systems are often generated via gaits, or cyclic motions in the shape space. Gaits transform a high-dimensional continuum search into a relatively tractable discrete search. In this paper, we put forward a framework which allows a planner to generate paths in a low dimensional work space and select among gaits, pre-planned motions in the robot's shape space. The contribution of this paper rests on the “virtual chassis” which is a choice of body frame for the snake robot that allows the planner to efficiently select among and plan with gaits to direct the robot along the work space path. We demonstrate this planner running on a simulated snake robot navigating through a variety of clutter scenarios. The virtual chassis also has the benefit of allowing us to generalize notions of controllability to gait motions.

We present an automated assembly system that directs the actions of a team of heterogeneous robots in the completion of an assembly task. From an initial user-supplied geometric specification, the system applies reasoning about the geometry of individual parts in order to deduce how they fit together. The task is then automatically transformed to a symbolic description of the assembly-a sort of blueprint. A symbolic planner generates an assembly sequence that can be executed by a team of collaborating robots. Each robot fulfills one of two roles: parts delivery or parts assembly. The latter are equipped with specialized tools to aid in the assembly process. Additionally, the robots engage in coordinated co-manipulation of large, heavy assemblies. We provide details of an example furniture kit assembled by the system.

We provide a set of sufficient conditions to establish the completeness of an ensemble of motion planners-that is, a set of loosely-coupled motion planners that produce a unified result. The planners are assumed to divide the total planning problem across some parameter space(s), such as task space, state space, action space, or time. Robotic applications have employed ensembles of planners for decades, although the concept has not been formally unified or analyzed until now. We focus on applications in multi-robot navigation and collision avoidance. We show that individual resolutionor probabilistically-complete planners that meet certain communication criteria constitute a (respectively, resolution- or probabilistically-) complete ensemble of planners. This ensemble of planners, in turn, guarantees that the robots are free of deadlock, livelock, and starvation.

Modern robots have to interact with their environment, search for objects, and move them around. Yet, for a robot to pick up an object, it needs to identify the object's orientation and locate it to within centimeter-scale accuracy. Existing systems that provide such information are either very expensive (e.g., the VICON motion capture system valued at hundreds of thousands of dollars) and/or suffer from occlusion and narrow field of view (e.g., computer vision approaches).

We present a distributed collision avoidance algorithm for multiple mobile robots that is model-predictive, sampling-based, and intuitive for operation around humans. Unlike purely reactive approaches, the proposed algorithm incorporates arbitrary trajectories as generated by a motion planner running on each navigating robot as well as predicted human trajectories. Our approach, inspired by human navigation in crowded pedestrian environments, draws from the sociology literature on pedestrian interaction. We propose a simple two-phase algorithm in which agents initially cooperate to avoid each other and then initiate civil inattention, thus lessening reactivity and committing to a trajectory. This process entails a pedestrian bargain in which all agents act competently to avoid each other and, once resolution is achieved, to avoid interfering with others' planned trajectories. This approach, being human-inspired, fluidly permits navigational interaction between humans and robots. We report experimental results for the algorithm running on real robots with and without human presence and in simulation.

We present the hardware design, software architecture, and core algorithms of Herb 2.0, a bimanual mobile manipulator developed at the Personal Robotics Lab at Carnegie Mellon University, Pittsburgh, PA. We have developed Herb 2.0 to perform useful tasks for and with people in human environments. We exploit two key paradigms in human environments: that they have structure that a robot can learn, adapt and exploit, and that they demand general-purpose capability in robotic systems. In this paper, we reveal some of the structure present in everyday environments that we have been able to harness for manipulation and interaction, comment on the particular challenges on working in human spaces, and describe some of the lessons we learned from extensively testing our integrated platform in kitchen and office environments.

We propose a motion planning algorithm that reasons about tradeoffs between the discrete decision problems and continuous optimization problems faced by a mobile robot navigating through a cluttered environment. Discrete decisions typically involve transient options as the robot selects corridors to traverse. By contrast, optimization occurs within open spaces among homotopic paths. We utilize properties of local path sets to detect decisions of immediate importance and select routes that maximize the chance of future success.

This paper describes a hierarchical planner deployed on a mobile manipulation system. The main idea is a two-level hierarchy combining a global planner which provides rough guidance to a local planner. We place a premium on fast response, so the global planner achieves speed by using a very rough approximation of the robot kinematics, and the local planner begins execution of the next action even without considering subsequent actions in detail, instead relying on the guidance of the global planner. The system exhibits few planning delays, and yet is surprisingly effective at planning collision free motions. The system is deployed on HERB, combining a Segway mobile platform, a WAM arm, and a Barrett hand. The navigation and manipulation components have been tested on the real robot, and the task of simultaneously approaching and grasping a bottle on a countertop was demonstrated in simulation.

The goal of motion planning is to find a feasible path that connects two positions and is free from collision with obstacles. Path sets are a robust approach to this problem in the face of real-world complexity and uncertainty. A path set is a collection of feasible paths and their corresponding control sequences. A path-set-based planner navigates by repeatedly testing each of these robot-fixed paths for collision with obstacles. A heuristic function selects which of the surviving paths to follow next. At each step, the robot follows a small piece of each path selected while simultaneously planning the subsequent trajectory. A path set possesses high path diversity if it performs well at obstacle-avoidance and goal-seeking behaviors. Previous work in path diversity has tacitly assumed that a correlation exists between this dynamic planning problem and a simpler, static path diversity problem: a robot placed randomly into an obstacle field evaluates its path set for collision a single time before following the chosen path in entirety. Although these problems might intuitively appear to be linked, this paper shows that static and dynamic path diversity are two distinct properties. After empirically demonstrating this fact, we discuss some of the factors that differentiate the two problems.

We present heuristic algorithms for pruning large sets of candidate paths or trajectories down to smaller subsets that maintain desirable characteristics in terms of overall reachability and path length. Consider the example of a set of candidate paths in an environment that is the result of a forward search tree built over a set of actions or behaviors. The tree is precomputed and stored in memory to be used online to compute collision-free paths from the root of the tree to a particular goal node. In general, such a set of paths may be quite large, growing exponentially in the depth of the search tree. In practice, however, many of these paths may be close together and could be pruned without a loss to the overall problem of path-finding. The best such pruning for a given resulting tree size is the one that maximizes path diversity, which is quantified as the probability of the survival of paths, averaged over all possible obstacle environments. We formalize this notion and provide formulas for computing it exactly. We also present experimental results for two approximate algorithms for path set reduction that are efficient and yield desirable properties in terms of overall path diversity. The exact formulas and approximate algorithms generalize to the computation and maximization of spatio-temporal diversity for trajectories.

This paper presents a solution to the problem of finding an effective yet admissible heuristic function for A* by precomputing a look-up table of solutions. This is necessary because traditional heuristic functions such as Euclidean distance often produce poor performance for certain problems. Here, the technique is applied to the state lattice, which is used for full state space motion planning. However, the approach is applicable to many applications of heuristic search algorithms. The look-up table is demonstrated to be feasible to generate and store. A principled technique is presented for selecting which queries belong in the table. Finally, the results are validated through testing on a variety of path planning problems

This paper describes a prototype robot and the necessary path planning and control for space inspection applications. The robot is the first generation of a free-flying robotic camera that will assist astronauts in constructing and maintaining the Space Station. The robot will provide remote views to astronauts inside the Space Shuttle and future Space Station, and to ground controllers. The paper describes a planar robot prototype autonomously moving about an air bearing table, and introduces a method for determining paths in three-dimensions for efficient fuel use. Finally, the paper describes the software simulation of the path planner with the future Space Station.