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.
Planning techniques can enable human robot collaboration when a precise model of the task is known, and might adapt to hidden user preferences as demonstrated by @cite_1 . Similarly, partially observable models can provide robustness to unpredicted events and account for unobservable states. Of particular note is the work by which, similarly to the approach presented in this paper, uses a POMDP to model a collaborative task. Indeed, POMDPs and similar models (e.g. MOMDPs) have been shown to improve robot assistance @cite_16 and team efficiency @cite_27 in related works. Such models of the task are however generally expensive to build and require advanced technical knowledge. Hence, a significant body of work in the fields of human-robot collaboration and physical human-robot interaction focuses on how to best take over the human partner by learning parts of the task that are burdensome in terms of physical safety or cognitive load. Under this perspective, the majority of the research in the field has focused on frameworks for learning new skills from human demonstration (LfD, @cite_9 ), efficiently learn or model task representations @cite_21 @cite_12 @cite_30 @cite_26 , or interpreting the human partner's actions and social signals @cite_29 .
@cite_30 @cite_26 @cite_9 @cite_21 @cite_1 @cite_29 @cite_27 @cite_16 @cite_12
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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. In this paper we present a framework that allows a human and a robot to perform simultaneous manipulation tasks safely in close proximity. The proposed framework is based on early prediction of the human's motion. The prediction system, which builds on previous work in the area of gesture recognition, generates a prediction of human workspace occupancy by computing the swept volume of learned human motion trajectories. The motion planner then plans robot trajectories that minimize a penetration cost in the human workspace occupancy while interleaving planning and execution. Multiple plans are computed in parallel, one for each robot task available at the current time, and the trajectory with the least cost is selected for execution. We test our framework in simulation using recorded human motions and a simulated PR2 robot. Our results show that our framework enables the robot to avoid the human while still accomplishing the robot's task, even in cases where the initial prediction of the human's motion is incorrect. We also show that taking into account the predicted human workspace occupancy in the robot's motion planner leads to safer and more efficient interactions between the user and the robot than only considering the human's current configuration. Planning under uncertainty faces a scalability problem when considering multi-robot teams, as the information space scales exponentially with the number of robots. To address this issue, this paper proposes to decentralize multi-robot partially observable Markov decision processes (POMDPs) while maintaining cooperation between robots by using POMDP policy auctions. Auctions provide a flexible way of coordinating individual policies modeled by POMDPs and have low communication requirements. In addition, communication models in the multi-agent POMDP literature severely mismatch with real inter-robot communication. We address this issue by exploiting a decentralized data fusion method in order to efficiently maintain a joint belief state among the robots. The paper presents two different applications: environmental monitoring with unmanned aerial vehicles (UAVs); and cooperative tracking, in which several robots have to jointly track a moving target of interest. The first one is used as a proof of concept and illustrates the proposed ideas through different simulations. The second one adds real multi-robot experiments, showcasing the flexibility and robust coordination that our techniques can provide. Trained human co-workers can often easily predict each other's intentions based on prior experience. When collaborating with a robot coworker, however, intentions are hard or impossible to infer. This difficulty of mental introspection makes human-robot collaboration challenging and can lead to dangerous misunderstandings. In this paper, we present a novel, object-aware projection technique that allows robots to visualize task information and intentions on physical objects in the environment. The approach uses modern object tracking methods in order to display information at specific spatial locations taking into account the pose and shape of surrounding objects. As a result, a human co-worker can be informed in a timely manner about the safety of the workspace, the site of next robot manipulation tasks, and next subtasks to perform. A preliminary usability study compares the approach to collaboration approaches based on monitors and printed text. The study indicates that, on average, the user effectiveness and satisfaction is higher with the projection based approach.