Library of actions: Implementing a generic robot execution framework by using manipulation action semantics

Aein, Mohamad Javad; Aksoy, Eren Erdal; Wörgötter, Florentin

doi:10.1177/0278364919850295

Cited by 23 publications

(12 citation statements)

References 45 publications

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“…Some other researches also demonstrated segmenting motion tasks could reduce the complexity that the lower modules have to deal with [120]. Whether it is by distinguishing between individual tasks based on relations of touch [5] or embedding scores or attractors directly into a robotic joint feature space [74]. Simplifying the problem of motion with several degrees of freedom, allows for a greater focus on the other parts.…”

Section: Avoiding Collisions With the Environmentmentioning

confidence: 99%

The State of Lifelong Learning in Service Robots:

Kasaei

Melsen

Beers

et al. 2021

J Intell Robot Syst

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Service robots are appearing more and more in our daily life. The development of service robots combines multiple fields of research, from object perception to object manipulation. The state-of-the-art continues to improve to make a proper coupling between object perception and manipulation. This coupling is necessary for service robots not only to perform various tasks in a reasonable amount of time but also to continually adapt to new environments and safely interact with non-expert human users. Nowadays, robots are able to recognize various objects, and quickly plan a collision-free trajectory to grasp a target object in predefined settings. Besides, in most of the cases, there is a reliance on large amounts of training data. Therefore, the knowledge of such robots is fixed after the training phase, and any changes in the environment require complicated, time-consuming, and expensive robot re-programming by human experts. Therefore, these approaches are still too rigid for real-life applications in unstructured environments, where a significant portion of the environment is unknown and cannot be directly sensed or controlled. In such environments, no matter how extensive the training data used for batch learning, a robot will always face new objects. Therefore, apart from batch learning, the robot should be able to continually learn about new object categories and grasp affordances from very few training examples on-site. Moreover, apart from robot self-learning, non-expert users could interactively guide the process of experience acquisition by teaching new concepts, or by correcting insufficient or erroneous concepts. In this way, the robot will constantly learn how to help humans in everyday tasks by gaining more and more experiences without the need for re-programming. In this paper, we review a set of previously published works and discuss advances in service robots from object perception to complex object manipulation and shed light on the current challenges and bottlenecks.

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Section: Avoiding Collisions With the Environmentmentioning

confidence: 99%

The State of Lifelong Learning in Service Robots:

Kasaei

Melsen

Beers

et al. 2021

J Intell Robot Syst

View full text Add to dashboard Cite

show abstract

“…Therefore, the actions are associated with the object set based on the subsystem-level affordance, which is defined as the affordance considering the robot's subsystem configuration. In this study, we assume that robots can acquire prior knowledge about the affordances and actions, as shown in Tables 1 and 2 with the development of the affordance classification methods [16], [17] and the action understanding methods [21], [22], Then, the actions are associated with the recognized object sets based on the prior knowledge and the robot's subsystem configuration.…”

Section: Association Of Action With Objectmentioning

confidence: 99%

ASTRON: Action-Based Spatio-Temporal Robot Navigation

2021

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To achieve the tasks provided by a user, it is necessary for robots to have a plan that fully exploits their functionalities in an environment. The objective of this study is to realize robot task planning in real space for effectively use of the robot's functions The plan is formed by deriving a feasible action sequence by interpreting the instructions within the scope of the action possibilities of the robots and the changes in them. In this paper, we first propose an action graph as a novel environmental representation approach to facilitate the understanding of the robot's action possibility in real space. In the action graph, the action possibility is represented by nodes, which describe the spatial position to perform each feasible action, and edges, which describe the feasible actions, based on the subsystem-level affordance and the arrangement of objects in the environment. We also propose an action-based spatio-temporal robot navigation (ASTRON), which focuses on robot navigation tasks. ASTRON enables the robots to determine a feasible action sequence that utilizes their functions by interpreting the instructions based on the action graph. The effectiveness of the proposed method was evaluated through simulations and actual machine experiments in a coffee shop environment. In the actual machine experiments, the proposed method was applied to robots with different subsystem configurations. The experimental results demonstrated that the proposed method could plan the feasible action sequence to complete the tasks by considering the environmental state and the subsystem configurations of the robot.

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“…In terms of spatial relations (as implemented in the current eSEC framework), the complexity of an action is far smaller than the complexity of the realm of objects with which an action can be performed, even when only considering a typical baby's environment. Clearly, this approach has proven to be beneficial for robotic applications [51] and we plan to extend it to complex actions and interactions between several agents (humans and robots) to examine the exploitation and exploration of predictive information during cooperation and competition.…”

Section: Plos Onementioning

confidence: 99%

Using enriched semantic event chains to model human action prediction based on (minimal) spatial information

et al. 2020

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Predicting other people’s upcoming action is key to successful social interactions. Previous studies have started to disentangle the various sources of information that action observers exploit, including objects, movements, contextual cues and features regarding the acting person’s identity. We here focus on the role of static and dynamic inter-object spatial relations that change during an action. We designed a virtual reality setup and tested recognition speed for ten different manipulation actions. Importantly, all objects had been abstracted by emulating them with cubes such that participants could not infer an action using object information. Instead, participants had to rely only on the limited information that comes from the changes in the spatial relations between the cubes. In spite of these constraints, participants were able to predict actions in, on average, less than 64% of the action’s duration. Furthermore, we employed a computational model, the so-called enriched Semantic Event Chain (eSEC), which incorporates the information of different types of spatial relations: (a) objects’ touching/untouching, (b) static spatial relations between objects and (c) dynamic spatial relations between objects during an action. Assuming the eSEC as an underlying model, we show, using information theoretical analysis, that humans mostly rely on a mixed-cue strategy when predicting actions. Machine-based action prediction is able to produce faster decisions based on individual cues. We argue that human strategy, though slower, may be particularly beneficial for prediction of natural and more complex actions with more variable or partial sources of information. Our findings contribute to the understanding of how individuals afford inferring observed actions’ goals even before full goal accomplishment, and may open new avenues for building robots for conflict-free human-robot cooperation.

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Library of actions: Implementing a generic robot execution framework by using manipulation action semantics

Cited by 23 publications

References 45 publications

The State of Lifelong Learning in Service Robots:

The State of Lifelong Learning in Service Robots:

ASTRON: Action-Based Spatio-Temporal Robot Navigation

Using enriched semantic event chains to model human action prediction based on (minimal) spatial information

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