Robotic assembly operation teaching in a virtual environment

Ogata, Hiroyuki; Takahashi, Tomoichi

doi:10.1109/70.294213

Cited by 63 publications

(23 citation statements)

References 12 publications

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“…In the 'what is being learned' area, most imitation learning research focuses on learning assembly/pick-and-place operations (Brand, 1997), (Ehrenmann, 2002), (Kang, 1991), (Kuniyoshi, 1994), (Ogata, 1994), (Paul, 1996), (Tung, 1995). Such research generally looks at an input data stream, and attempts to segment the stream to identify actions performed by a human hand.…”

Section: Related Workmentioning

confidence: 99%

“…These include hidden Markov models/finite state automata (Brand, 1997), (Ogata, 1994), (Paul, 1996); relational/contact expression grammars (Kuniyoshi, 1994), (Pangburn, 1994), (Siskind, 2000), (Tung, 1995), in which properties such as 'is-picking-up', 'is-touching' and 'infront-of' are used to reason about the state of assembly tasks; contact wrenches (Pollard, 2002), in which a task is represented by a sequence of torques at contact points; contact locations (Kang, 1993), (Kang, 1991) or locations and forces together (Ehrenmann, 2002) of a hand on an object, which are used to determine which grasp in a taxonomy is being used; perceptuo-motor primitives consisting of lines and arcs of motion in the air (Jenkins, 2000) or air hockey movements (Bentivegna, 2002); linear quadratic regulators (LQR) for pole-balancing (Schall, 1997); or an assortment of properties related to specific verbs like push or shove, such as elbow extension or acceleration (Bailey, 1998).…”

Section: Related Workmentioning

confidence: 99%

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Imitation Learning of Whole-Body Grasps

Hsiao¹,

Lozano-Pérez²

2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Humans often learn to manipulate objects by observing other people. In much the same way, robots can use imitation learning to pick up useful skills. A system is demonstrated here for using imitation learning to teach a robot to grasp objects using both hand and whole-body grasps, which use the arms and torso as well as hands. Demonstration grasp trajectories are created by teleoperating a simulated robot to pick up simulated objects, and stored as sequences of keyframes in which contacts with the object are gained or lost. When presented with a new object, the system compares it against the objects in a stored database to pick a demonstrated grasp used on a similar object. Both objects are modeled as a combination of primitives-boxes, cylinders, and spheres-and the primitives for each object are grouped into 'functional groups' that geometrically match parts of the new object with similar parts of the demonstration object. These functional groups are then used to map contact points from the demonstration object to the new object, and the resulting adapted keyframes are adjusted and checked for feasibility. Finally, a trajectory is found that moves among the keyframes in the adapted grasp sequence, and the full trajectory is tested for feasibility by executing it in the simulation. The system successfully uses this method to pick up 92 out of 100 randomly generated test objects in simulation.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Imitation Learning of Whole-Body Grasps

Hsiao¹,

Lozano-Pérez²

2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…[Myers 1999] develops a teach-by-showing approach that monitors both sensor inputs to the robot controller and the control outputs from the controller during a training session to automatically generate sensor-based autonomous programs from taught motions. [ [Shimokura & Liu 1994], [Ogata & Takahashi 1994], [Jagersand & Nelson 1995], and [Rafflin & Fournier 1996]. Demonstration can also be used to train robots to perform at a higher level of abstraction.…”

Section: Teaching By Demonstrationmentioning

confidence: 99%

Active Research Topics in Human Machine Interfaces

McDonald¹

2000

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This paper identifies active research topics concerning human machine interfaces for intelligent machine systems. The paper was compiled by performing a series of literature searches and organizing the information according to the author's interest in better directing his own Human Machine Interface (HMI) research. Introductory literature from outside the HMI communities is also referenced to provide context.

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“…In such cases, the robots reaction to environmental stimulus are designed by user criteria [Sangpetch, 2005]. Moreover, the robot can support an auto-designing behavior which assimilates user instructions referred to an initial trajectory [Ogata and Takahashi, 1994]. This type of control platform adapts to processing resources and communication schemes for realtime or remote robot control.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Localization-Oriented Hri for Walking Robots in the Robocup Environment

Samperio

2008

Int. J. Inf. Acquisition

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Received AcceptedThis paper presents the design and implementation of a human-robot interface capable of evaluating robot localization performance and maintaining full control of robot behaviors in the RoboCup domain. The system consists of legged robots, behavior modules, an overhead visual tracking system, and a graphic user interface. A human-robot communication framework is designed for executing cooperative and competitive processing tasks between users and robots by using object oriented and modularised software architecture, operability, and functionality. Some experimental results are presented to show the performance of the proposed system based on simulated and real-time information.

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Robotic assembly operation teaching in a virtual environment

Cited by 63 publications

References 12 publications

Imitation Learning of Whole-Body Grasps

Imitation Learning of Whole-Body Grasps

Active Research Topics in Human Machine Interfaces

Implementation of a Localization-Oriented Hri for Walking Robots in the Robocup Environment

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