Robot programming by human demonstration: the use of human inconsistency in improving 3D robot trajectories

Delson, Nathan; West, Harry

doi:10.1109/iros.1994.407519

Cited by 27 publications

(24 citation statements)

References 7 publications

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“…A volume swept by manipulator's bodies stands for (a part of) the free space of the manipulator, because the manipulator has passed through the volume without collisions. Similar idea can be also found in [10], where a swept volume by the manipulated object is used to generate collision-free paths. In this paper, we apply Hasegawa's idea to ordinary robot programming, by using manual volume sweeping instead of teleoperation.…”

Section: Overview Of Proposed Robot Programmingmentioning

confidence: 96%

Easy robot programming for industrial manipulators by manual volume sweeping

Maeda

Ushioda

Makita

2008

2008 IEEE International Conference on Robotics and Automation

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Abstract-In this paper, we propose a robot programming method for industrial manipulators. It enables novice human operators to teach "good" robot motions in a short period of time. First in this method, a human operator makes a robot manipulator sweep a volume by its bodies. The manipulator is equipped with a force sensor on its wrist and dampingcontrolled; the operator can move it freely by grasping its end-effector. The swept volume stands for a part of the manipulator's free space, because the manipulator has passed through the volume without collisions. Next, the obtained swept volume is used by a motion planner to generate a well-optimized path of the manipulator automatically. Finally, the planned motion is executed by the manipulator. Even non-skilled operators can generate robot motions with short cycle time by doing the above procedure. The effectiveness of our method is successfully demonstrated in teaching experiments for a comparison with other conventional methods.

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Section: Overview Of Proposed Robot Programmingmentioning

confidence: 96%

Easy robot programming for industrial manipulators by manual volume sweeping

Maeda

Ushioda

Makita

2008

2008 IEEE International Conference on Robotics and Automation

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“…Invariants among the demonstrations are seen as the most relevant and selected as essential components of the task [3,17]. Several methods for discovering invariants in demonstrations can be found in the LFD literature.…”

Section: Motivationmentioning

confidence: 99%

Predictive Learning from Demonstration

Billing

Hellström

Janlert

2011

Communications in Computer and Information Science

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Abstract.A model-free learning algorithm called Predictive Sequence Learning (PSL) is presented and evaluated in a robot Learning from Demonstration (LFD) setting. PSL is inspired by several functional models of the brain. It constructs sequences of predictable sensory-motor patterns, without relying on predefined higher-level concepts. The algorithm is demonstrated on a Khepera II robot in four different tasks. During training, PSL generates a hypothesis library from demonstrated data. The library is then used to control the robot by continually predicting the next action, based on the sequence of passed sensor and motor events. In this way, the robot reproduces the demonstrated behavior. PSL is able to successfully learn and repeat three elementary tasks, but is unable to repeat a fourth, composed behavior. The results indicate that PSL is suitable for learning problems up to a certain complexity, while higher level coordination is required for learning more complex behaviors.

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“…The discussion about what it means to repeat a behavior becomes complicated further when the robot acts in a dynamic, non-deterministic and partially accessible [77, ch.2] environment. Demonstrated event sequences may be both incomplete and contain mistakes that should not be learned or repeated [28]. If the robot manages to successfully repeat a demonstrated behavior under different conditions than during the demonstration we say that the robot is able to generalize the demonstrated behavior.…”

Section: Case 1 Corresponds To What Is Often Called Action-level Imitmentioning

confidence: 99%

A Formalism for Learning from Demonstration^*

Billing

Hellström

2010

Paladyn, Journal of Behavioral Robotics

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The paper describes and formalizes the concepts and assumptions involved in Learning from Demonstration (LFD), a common learning technique used in robotics. LFD-related concepts like goal, generalization, and repetition are here defined, analyzed, and put into context. Robot behaviors are described in terms of trajectories through information spaces and learning is formulated as mappings between some of these spaces. Finally, behavior primitives are introduced as one example of good bias in learning, dividing the learning process into the three stages of behavior segmentation, behavior recognition, and behavior coordination. The formalism is exemplified through a sequence learning task where a robot equipped with a gripper arm is to move objects to specific areas. The introduced concepts are illustrated with special focus on how bias of various kinds can be used to enable learning from a single demonstration, and how ambiguities in demonstrations can be identified and handled.

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Robot programming by human demonstration: the use of human inconsistency in improving 3D robot trajectories

Cited by 27 publications

References 7 publications

Easy robot programming for industrial manipulators by manual volume sweeping

Easy robot programming for industrial manipulators by manual volume sweeping

Predictive Learning from Demonstration

A Formalism for Learning from Demonstration^*

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Robot programming by human demonstration: the use of human inconsistency in improving 3D robot trajectories

Cited by 27 publications

References 7 publications

Easy robot programming for industrial manipulators by manual volume sweeping

Easy robot programming for industrial manipulators by manual volume sweeping

Predictive Learning from Demonstration

A Formalism for Learning from Demonstration*

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Product

Resources

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A Formalism for Learning from Demonstration^*