Cognitive Reasoning for Compliant Robot Manipulation

Leidner, Daniel

doi:10.1007/978-3-030-04858-7

Cited by 25 publications

(26 citation statements)

References 97 publications

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“…This is important to show the general nature of our system: the way in which we connect visual interpretation of objects to adaptation of motions can be applied to many tasks. This contrasts with many leading robotics works which focus on a single task [6], [7].…”

Section: Introductionmentioning

confidence: 89%

Adapting Everyday Manipulation Skills to Varied Scenarios

Gajewski

Ferreira

Bartels

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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We address the problem of executing tool-using manipulation skills in scenarios where the objects to be used may vary. We assume that point clouds of the tool and target object can be obtained, but no interpretation or further knowledge about these objects is provided. The system must interpret the point clouds and decide how to use the tool to complete a manipulation task with a target object; this means it must adjust motion trajectories appropriately to complete the task. We tackle three everyday manipulations: scraping material from a tool into a container, cutting, and scooping from a container. Our solution encodes these manipulation skills in a generic way, with parameters that can be filled in at runtime via queries to a robot perception module; the perception module abstracts the functional parts of the tool and extracts key parameters that are needed for the task. The approach is evaluated in simulation and with selected examples on a PR2 robot.

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Section: Introductionmentioning

confidence: 89%

Adapting Everyday Manipulation Skills to Varied Scenarios

Gajewski

Ferreira

Bartels

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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“…Finally, full autonomy approaches have also been successful for many tasks, ranging from feeding [19] to cleaning [20] and pancake flipping [21]. This latter work, as well as some of the above examples, uses active constraints.…”

Section: Related Workmentioning

confidence: 99%

Shared Control Templates for Assistive Robotics

Quere

Hagengruber

Iskandar

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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Lightweight robotic manipulators can be used to restore the manipulation capability of people with a motor disability. However, manipulating the environment poses a complex task, especially when the control interface is of low bandwidth, as may be the case for users with impairments. Therefore, we propose a constraint-based shared control scheme to define skills which provide support during task execution. This is achieved by representing a skill as a sequence of states, with specific user command mappings and different sets of constraints being applied in each state. New skills are defined by combining different types of constraints and conditions for state transitions, in a human-readable format. We demonstrate its versatility in a pilot experiment with three activities of daily living. Results show that even complex, high-dimensional tasks can be performed with a low-dimensional interface using our shared control approach.

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“…In previous work we have shown the advantages of organizing the knowledge of the robot in an object-centered context [25]. The information required for the robot to understand its environment and manipulate objects, is stored with the objects.…”

Section: A Semi-autonomous Crew Assistance Robotmentioning

confidence: 99%

Knowledge Driven Orbit-to-Ground Teleoperation of a Robot Coworker

Schmaus

Leidner

Krüger

et al. 2020

IEEE Robot. Autom. Lett.

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The crewed exploration of Moon and Mars requires the construction and maintenance of infrastructure on the alien surfaces before a crew arrives. Robotic coworkers are envisioned to take over the physical labor required to set-up crew habitats, energy supplies, and return vehicles in the hazardous environment. Deploying these robots in such a remote location poses a challenge that requires autonomous robot capabilities in combination with effective Human Robot Interfaces (HRIs), which comply with the harsh conditions of deep space operations. An astronaut-robot teleoperation concept targeting these topics has been evaluated in DLR and ESA's METERON SUPVIS Justin experiment where astronauts on-board the International Space Station (ISS) commanded DLR's humanoid robot Rollin' Justin in a simulated Martian environment on Earth. This work extends on our previously presented approach to supervised autonomy. It examines the results of the two follow-up experiment sessions which investigated maintenance and assembly tasks in real-world scenarios. We discuss the use of our system in real space-toground deployment and analyze key performance metrics of the HRI and the feedback given by the astronauts.

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Cognitive Reasoning for Compliant Robot Manipulation

Cited by 25 publications

References 97 publications

Adapting Everyday Manipulation Skills to Varied Scenarios

Adapting Everyday Manipulation Skills to Varied Scenarios

Shared Control Templates for Assistive Robotics

Knowledge Driven Orbit-to-Ground Teleoperation of a Robot Coworker

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