Movement-aware action control &amp;#x2014; Integrating symbolic and control-theoretic action execution

Kresse, Ingo; Beetz, Michael

doi:10.1109/icra.2012.6225119

Cited by 16 publications

(13 citation statements)

References 14 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Describing lower level motions, accelerations and forces would however allow the exchange of novel actions that are not available as executable modules on the target robot. One option that we are currently exploring is to include symbolic motion constraints that can be interpreted by a motion controller [33].…”

Section: Discussionmentioning

confidence: 99%

Representation and Exchange of Knowledge About Actions, Objects, and Environments in the RoboEarth Framework

Tenorth¹,

Perzylo²,

Lafrenz³

et al. 2013

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

Abstract-The community-based generation of content has been tremendously successful in the World-Wide Web-people help each other by providing information that could be useful to others. We are trying to transfer this approach to robotics in order to help robots acquire the vast amounts of knowledge needed to competently perform everyday tasks. ROBOEARTH is intended to be a web community by robots for robots to autonomously share descriptions of tasks they have learned, object models they have created, and environments they have explored. In this paper, we report on the formal language we developed for encoding this information and present our approaches to solve the inference problems related to finding information, to determining if information is usable by a robot, and to grounding it on the robot platform.Note to Practitioners-In this paper, we report on a formal language for knowledge representation that is used in the ROBOEARTH system, a web-based knowledge base intended to be like a "Wikipedia for robots." The objective is to enable robots to share information about how to perform actions, how to recognize and interact with objects, and where to find objects in an environment. The developed language allows to store such information in a format that supports logical inference, so that robots can for example autonomously decide if they have all prerequisites needed for performing a described action. In laboratory experiments, the system has been applied to the exchange of pick-and-place style activities between two mobile manipulation robots. We are currently extending the representation towards more fine-grained action specifications.

show abstract

Section: Discussionmentioning

confidence: 99%

Representation and Exchange of Knowledge About Actions, Objects, and Environments in the RoboEarth Framework

Tenorth¹,

Perzylo²,

Lafrenz³

et al. 2013

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…Research in this area is done utilizing either task functions [4], [5] or virtual joints [6]. In iTaSC [4] the task function is defined over the pose of the object that is to be manipulated.…”

Section: Related Workmentioning

confidence: 99%

“…Constraint analysis applied to robotics can be split in two groups: constraints on trajectory [1], [2], [3] and constraints on object pose [4], [5], [6]. Georgiev et al in [1] considered trajectory constraint detection using only proprioceptive data, joint torques of the robot's arm.…”

Section: Related Workmentioning

confidence: 99%

Spatial constraint identification of parts in SE3 for action optimization

Jørgensen

Rukavishnikova

Krüger

et al. 2015

2015 IEEE International Conference on Industrial Technology (ICIT)

View full text Add to dashboard Cite

In this paper we present a method to structure contextual knowledge in spatial regions/manifolds that may be used in action selection for industrial robotic systems. The contextual knowledge is build on relatively few prior task executions and it may be derived from either teleoperation or previous action executions. We argue that our contextual representation is able to improve the execution speed of individual actions and demonstrate this on a specific time-consuming action of object detection and pose estimation.Our contextual knowledge representation is especially suited for industrial environments where repetitive tasks such as binand belt picking are plentiful. We present how we classify and detect the contextual information from prior task executions and demonstrate the performance gain on a real industrial pick-and-place problem.

show abstract

“…Beetz et al [2010] outline an ambitious system for downloading abstract task instructions from the web and using common-sense knowledge to expand them into detailed plans ultimately executable on a mobile manipulator. In the context of that project, Kresse and Beetz [2012] specify a system that combines logical reasoning with rich force-control primitives: the high-level planner specifies constraints on motions and the low-level planner exploits the null space of those constraints to find feasible detailed control policies for the robot.…”

Section: Integrating Logical and Geometric Planningmentioning

confidence: 99%

Integrated Robot Task and Motion Planning in the Now

Kaelbling¹,

Lozano-Pérez

2012

View full text Add to dashboard Cite

Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. This paper provides an approach to integrating geometric motion planning with logical task planning for long-horizon tasks in domains with many objects. We propose a tight integration between the logical and geometric aspects of planning. We use a logical representation which includes entities that refer to poses, grasps, paths and regions, without the need for a priori discretization. Given this representation and some simple mechanisms for geometric inference,we characterize the pre-conditions and effects of robot actions in terms of these logical entities. We then reason about the interaction of the geometric and non-geometric aspects of our domains using the general-purpose mechanism of goal regression (also known as pre-image backchaining). We propose an aggressive mechanism for temporal hierarchical decomposition, which postpones the pre-conditions of actions to create an abstraction hierarchy that both limits the lengths of plans that need to be generated and limits the set of objects relevant to each plan. We describe an implementation of this planning method and demonstrate it in a simulated kitchen environment in which it solves problems that require approximately 100 individual pick or place operations for moving multiple objects in a complex domain. This paper provides an approach to integrating geometric motion planning with logical task planning for long-horizon tasks in domains with many objects. We propose a tight integration between the logical and geometric aspects of planning. We use a logical representation which includes entities that refer to poses, grasps, paths and regions, without the need for a priori discretization. Given this representation and some simple mechanisms for geometric inference, we characterize the pre-conditions and effects of robot actions in terms of these logical entities.We then reason about the interaction of the geometric and non-geometric aspects of our domains using the general-purpose mechanism of goal regression (also known as pre-image backchaining). We propose an aggressive mechanism for temporal hierarchical decomposition, which postpones the pre-conditions of actions to create an abstraction hierarchy that both limits the lengths of plans that need t...

show abstract

Movement-aware action control — Integrating symbolic and control-theoretic action execution

Cited by 16 publications

References 14 publications

Representation and Exchange of Knowledge About Actions, Objects, and Environments in the RoboEarth Framework

Representation and Exchange of Knowledge About Actions, Objects, and Environments in the RoboEarth Framework

Spatial constraint identification of parts in SE3 for action optimization

Integrated Robot Task and Motion Planning in the Now

Contact Info

Product

Resources

About