The field of Human-Robot Collaboration (HRC) has seen a considerable amount of progress in the recent years. Although genuinely collaborative platforms are far from being deployed in real-world scenarios, advances in control and perception algorithms have progressively popularized robots in manufacturing settings, where they work side by side with human peers to achieve shared tasks. Unfortunately, little progress has been made toward the development of systems that are proactive in their collaboration, and autonomously take care of some of the chores that compose most of the collaboration tasks. In this work, we present a collaborative system capable of assisting the human partner with a variety of supportive behaviors in spite of its limited perceptual and manipulation capabilities and incomplete model of the task. Our framework leverages information from a high-level, hierarchical model of the task. The model, that is shared between the human and robot, enables transparent synchronization between the peers and understanding of each other's plan. More precisely, we derive a partially observable Markov model from the high-level task representation. We then use an online solver to compute a robot policy, that is robust to unexpected observations such as inaccuracies of perception, failures in object manipulations, as well as discovers hidden user preferences. We demonstrate that the system is capable of robustly providing support to the human in a furniture construction task.
No matter how efficient such models are at exhibiting the intended behavior, they are often limited to simple tasks and are not transparent to the human peer. Indeed, evidences from the study of human-human interactions have demonstrated the importance of sharing mental task models to improve the efficiency of the collaboration @cite_0 . Similarly, studies on human-robot interactions show that an autonomous robot with a model of the task shared with a human peer can decrease the idle time for the human during the collaboration @cite_28 . Without enabling the robot to learn the task, other approaches have demonstrated the essential capability for collaborative robots to dynamically adapt their plans with respect to the task in order to accommodate for human's actions or unforeseen events @cite_18 . Likewise, rich tasks models can also enable the optimization of the decision with respect to extrinsic metrics such as risk on the human @cite_19 or completion time @cite_5 .
@cite_18 @cite_28 @cite_0 @cite_19 @cite_5
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Our human-robot collaboration research aims to improve the fluency and efficiency of interactions between humans and robots when executing a set of tasks in a shared workspace. During human-robot collaboration, a robot and a user must often complete a disjoint set of tasks that use an overlapping set of objects, without using the same object simultaneously. A key challenge is deciding what task the robot should perform next in order to facilitate fluent and efficient collaboration. Most prior work does so by first predicting the human's intended goal, and then selecting actions given that goal. However, it is often difficult, and sometimes impossible, to infer the human's exact goal in real time, and this serial predict-then-act method is not adaptive to changes in human goals. In this paper, we present a system for inferring a probability distribution over human goals, and producing assistance actions given that distribution in real time. The aim is to minimize the disruption caused by the nature of human-robot shared workspace. We extend recent work utilizing Partially Observable Markov Decision Processes (POMDPs) for shared autonomy in order to provide assistance without knowing the exact goal. We evaluate our system in a study with 28 participants, and show that our POMDP model outperforms state of the art predict-then-act models by producing fewer human-robot collisions and less human idling time. We consider the problem of navigating a mobile robot through dense human crowds. We begin by exploring a fundamental impediment to classical motion planning algorithms called the âfreezing robot problemâ: once the environment surpasses a certain level of dynamic complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place or performs unnecessary maneuvers to avoid collisions. We argue that this problem can be avoided if the robot anticipates human cooperation, and accordingly we develop interacting Gaussian processes, a prediction density that captures cooperative collision avoidance, and a âmultiple goalâ extension that models the goal-driven nature of human decision making. We validate this model with an empirical study of robot navigation in dense human crowds 488 runs, specifically testing how cooperation models effect navigation performance. The multiple goal interacting Gaussian processes algorithm performs comparably with human teleoperators in crowd densities nearing 0.8 humans m2, while a state-of-the-art non-cooperative planner exhibits unsafe behavior more than three times as often as the multiple goal extension, and twice as often as the basic interacting Gaussian process approach. Furthermore, a reactive planner based on the widely used dynamic window approach proves insufficient for crowd densities above 0.55 people m2. We also show that our non-cooperative planner or our reactive planner capture the salient characteristics of nearly any dynamic navigation algorithm. Based on these experimental results and theoretical observations, we conclude that a cooperation model is critical for safe and efficient robot navigation in dense human crowds. The goal of this work is to develop computational models of social intelligence that enable robots to work side by side with humans, solving problems and achieving task goals through dialogue and collaborative manipulation. A defining problem of collaborative behavior in an embodied setting is the manner in which multiple agents make use of shared resources. In a situated dialogue, these resources include physical bottlenecks such as objects or spatial regions, and cognitive bottlenecks such as the speaking floor. For a robot to function as an effective collaborative partner with a human, it must be able to seize and yield such resources appropriately according to social expectations. We describe a general framework that uses timed Petri nets for the modeling and execution of robot speech, gaze, gesture, and manipulation for collaboration. The system dynamically monitors resource requirements and availability to control real-time turn-taking decisions over resources that are shared with humans, reasoning about different resource types independently. We evaluate our approach with an experiment in which our robot Simon performs a collaborative assembly task with 26 different human partners, showing that the multimodal reciprocal approach results in superior task performance, fluency, and balance of control. Collaborative robots represent a clear added value to manufacturing, as they promise to increase productivity and improve working conditions of such environments. Although modern robotic systems have become safe and reliable enough to operate close to human workers on a day-to-day basis, the workload is still skewed in favor of a limited contribution from the robot's side, and a significant cognitive load is allotted to the human. We believe the transition from robots as recipients of human instruction to robots as capable collaborators hinges around the implementation of transparent systems, where mental models about the task are shared between peers, and the human partner is freed from the responsibility of taking care of both actors. In this work, we implement a transparent task planner able to be deployed in realistic, near-future applications. The proposed framework is capable of basic reasoning capabilities for what concerns role assignment and task allocation, and it interfaces with the human partner at the level of abstraction he is most comfortable with. The system is readily available to non-expert users, and programmable with high-level commands in an intuitive interface. Our results demonstrate an overall improvement in terms of completion time, as well as a reduced cognitive load for the human partner.