In this paper, we present a cyber-physical testbed created to enable a human-robot team to preform a shared task in a shared workspace. The testbed is suitable for the implementation of a tabletop manipulation task, a common human-robot collaboration scenario. The testbed integrates elements that exists in the physical and virtual world. In this work, we report the insights we gathered through out our exploration in understanding and implementing task planning and execution for human-robot team.
Task planning is a key ability for intelligent robotic systems, increasing their autonomy through the construction of sequences of actions to achieve a final goal. However, when working in a team, planning for the sequence of actions without taking into consideration the actions of the other partner is not enough, especially when collaborating in achieving a common task. Therefore, adaptability and flexibility of planning are crucial in such scenarios. This has been investigated extensively in previous work. Our testbed is inspired by this work @cite_1 , that offers a comprehensive system that identifies and integrates individual and collaborative human inspired cognitive skills a robot should have to share space and task with a human partner. The scenario we are interested in is similar to the one investigated by @cite_3 , where a robot and a human manipulate an overlapping set of objects, without using the same object simultaneously. A key element in task planning and execution is the robot ability to recognized and anticipate the human's actions. Similar to other work in the literature @cite_2 @cite_8 , we recognize and anticipate human actions through the human arm motion within the workspace to enable task planning and execution.
@cite_8 @cite_1 @cite_3 @cite_2
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Allowing a cobot to predict what the human operator is about to do can definitely enhance the effectiveness of human-robot collaboration. This paper addresses the problem of inferring the most likely reaching target of the human hand. The method allows the robot to promptly recognise the intention of the human to reach a certain position within the scene and can be thus used by the controller of the robot to take the optimal decision on what to do. A novel method based on Bayesian statistics has been developed in this work and its applicability in a realistic context has been verified within an industrial use case, consisting of a commercial collaborative robot and a human operator performing a collaborative assembly task. HumanRobot Interaction challenges Artificial Intelligence in many regards: dynamic, partially unknown environments that were not originally designed for robots; a broad variety of situations with rich semantics to understand and interpret; physical interactions with humans that requires fine, low-latency yet socially acceptable control strategies; natural and multi-modal communication which mandates common-sense knowledge and the representation of possibly divergent mental models. This article is an attempt to characterise these challenges and to exhibit a set of key decisional issues that need to be addressed for a cognitive robot to successfully share space and tasks with a human.We identify first the needed individual and collaborative cognitive skills: geometric reasoning and situation assessment based on perspective-taking and affordance analysis; acquisition and representation of knowledge models for multiple agents (humans and robots, with their specificities); situated, natural and multi-modal dialogue; human-aware task planning; humanrobot joint task achievement. The article discusses each of these abilities, presents working implementations, and shows how they combine in a coherent and original deliberative architecture for humanrobot interaction. Supported by experimental results, we eventually show how explicit knowledge management, both symbolic and geometric, proves to be instrumental to richer and more natural humanrobot interactions by pushing for pervasive, human-level semantics within the robot's deliberative system. 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. Interest in human-robot coexistence, in which humans and robots share a common work volume, is increasing in manufacturing environments. Efficient work coordination requires both awareness of the human pose and a plan of action for both human and robot agents in order to compute robot motion trajectories that synchronize naturally with human motion. In this paper, we present a data-driven approach that synthesizes anticipatory knowledge of both human motions and subsequent action steps in order to predict in real-time the intended target of a human performing a reaching motion. Motion-level anticipatory models are constructed using multiple demonstrations of human reaching motions. We produce a library of motions from human demonstrations, based on a statistical representation of the degrees of freedom of the human arm, using time series analysis, wherein each time step is encoded as a multivariate Gaussian distribution. We demonstrate the benefits of this approach through offline statistical analysis of human motion data. The results indicate a considerable improvement over prior techniques in early prediction, achieving 70 or higher correct classification on average for the first third of the trajectory (< 500msec). We also indicate proof-of-concept through the demonstration of a human-robot cooperative manipulation task performed with a PR2 robot. Finally, we analyze the quality of task-level anticipatory knowledge required to improve prediction performance early in the human motion trajectory.