Cooking for humanoid robot, a task that needs symbolic and geometric reasonings

Gravot, Fabien; Haneda, A.; Okada, Kei; Inaba, Masayuki

doi:10.1109/robot.2006.1641754

Cited by 24 publications

(19 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Assuming that the house is equipped with a housekeeping robot (similar to the cooking assistant described in Gravot et al [2006]) able of performing basic recognition and manipulation tasks, such as moving around, getting and putting items at particular places, sensing their temperature, etc., then the request of the user can be fulfilled by the robot. Let's say that there are no beers in the fridge, however, the system finds out that there are some beers left on the storage shelf; the assumption is that items in the house are labeled by RFID tags, and a smart fridge and smart shelves keep track of them.…”

Section: A Day In the Smart Homementioning

confidence: 99%

“…The requests the user can make to the system are limited to a set of rather simple commands, which only involve the interaction of a limited set of predefined agents. A hierarchical task network planning approach is adopted in Gravot et al [2006] for controlling a humanoid robot so that it performs certain cooking tasks. The planner bases on the description of predefined methods expressed in terms of the actions the robot can perform.…”

Section: Service Composition In Pervasive Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Coordinating the web of services for a smart home

et al. 2013

View full text Add to dashboard Cite

Domotics, concerned with the realization of intelligent home environments, is a novel field which can highly benefit from solutions inspired by service-oriented principles to enhance the convenience and security of modern home residents. In this work, we present an architecture for a smart home, starting from the lower device interconnectivity level up to the higher application layers that undertake the load of complex functionalities and provide a number of services to end-users. We claim that in order for smart homes to exhibit a genuinely intelligent behavior, the ability to compute compositions of individual devices automatically and dynamically is paramount. To this end, we incorporate into the architecture a composition component that employs artificial intelligence domain-independent planning to generate compositions at runtime, in a constantly evolving environment. We have implemented a fully working prototype that realizes such an architecture, and have evaluated it both in terms of performance as well as from the end-user point of view. The results of the evaluation show that the service-oriented architectural design and the support for dynamic compositions is quite efficient from the technical point of view, and that the system succeeds in satisfying the expectations and objectives of the users.

show abstract

Section: A Day In the Smart Homementioning

confidence: 99%

Section: Service Composition In Pervasive Systemsmentioning

confidence: 99%

Coordinating the web of services for a smart home

et al. 2013

View full text Add to dashboard Cite

show abstract

“…Consider autonomous personal robots that are to perform housework ( [1,2,3]). Such a robot has to set the table.…”

Section: Introductionmentioning

confidence: 99%

Understanding and executing instructions for everyday manipulation tasks from the World Wide Web

Tenorth

Nyga

Beetz

2010

2010 IEEE International Conference on Robotics and Automation

103

View full text Add to dashboard Cite

Service robots will have to accomplish more and more complex, open-ended tasks and regularly acquire new skills. In this work, we propose a new approach to generating plans for such household robots. Instead composing them from atomic actions, we propose to transform task descriptions on web sites like ehow.com into executable robot plans. We present methods for automatically converting the instructions given in natural language into a formal, logic-based representation, for resolving the word senses using the WordNet database and the Cyc ontology, and for exporting the generated plans into the mobile robot's plan language RPL. We discuss the problems of inferring information missing in these descriptions, of grounding the abstract task descriptions in the perception and action system, and we propose techniques for solving them. The whole system works autonomously without human interaction. It has successfully been tested with a set of about 150 natural language directives, of which up to 80% could be correctly transformed.

show abstract

“…In the first stage, all the sensors agree on a common snapshot of the environment and pass this information to the planners. The planning process then selects target grasps and computes possible configuration goals for the robot [1,2]. A global plan for the geometric motion of the robot is then created by enforcing any task specific constraints.…”

Section: Introductionmentioning

confidence: 99%

Integrating grasp planning and visual feedback for reliable manipulation

Diankov

Kanade

Kuffner

2009

2009 9th IEEE-RAS International Conference on Humanoid Robots

View full text Add to dashboard Cite

Abstract-We present a vision-centric manipulation framework for reliably performing reach-and-grasp tasks in everyday environments. By combining grasp planning and visual feedback algorithms, and constantly considering sensor visibility, the framework can recover from sensor calibration errors and unexpected changes in the environment. Although many current robot systems include planning and vision components, these components are treated independently, which reduces the capability of the system from making informed decisions. Our proposed framework incorporates information in a data-driven way from both planning and vision modalities during the planning and execution phases of the task. The planning phase generates a plan to move the robot manipulator as close as safely possible to the target object such that the target is easily detectable by the on-board sensors. The execution phase is responsible for continuously choosing and validating a grasp for the target while updating the environment with more accurate information. We stress the importance of performing grasp selection for the target during visual-feedback execution because more precise information about the target's location and its surroundings is available. We evaluate our framework on several robot platforms in simulation.

show abstract

Cooking for humanoid robot, a task that needs symbolic and geometric reasonings

Cited by 24 publications

References 8 publications

Coordinating the web of services for a smart home

Coordinating the web of services for a smart home

Understanding and executing instructions for everyday manipulation tasks from the World Wide Web

Integrating grasp planning and visual feedback for reliable manipulation

Contact Info

Product

Resources

About