Motion learning in variable environments using probabilistic flow tubes

Dong, Shuonan; Williams, Brian

doi:10.1109/icra.2011.5980530

Cited by 14 publications

(3 citation statements)

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“…Teleoperation has the advantage of establishing an efficient communication and operation strategy between humans and robots. It has been applied in various applications, including remote control of a mobile robotic assistant [70][71][72], performing an assembly task [68,73,74], performing a spatial-positioning task [75], demonstrating grasp preshapes to the robot [76], transmitting both dynamic and communicative information on a collaborative task [77], and picking and moving tasks [78].…”

Section: Teleoperated Demonstrationmentioning

confidence: 99%

Robot Learning from Demonstration in Robotic Assembly: A Survey

Zhu

2018

Robotics

186

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Learning from demonstration (LfD) has been used to help robots to implement manipulation tasks autonomously, in particular, to learn manipulation behaviors from observing the motion executed by human demonstrators. This paper reviews recent research and development in the field of LfD. The main focus is placed on how to demonstrate the example behaviors to the robot in assembly operations, and how to extract the manipulation features for robot learning and generating imitative behaviors. Diverse metrics are analyzed to evaluate the performance of robot imitation learning. Specifically, the application of LfD in robotic assembly is a focal point in this paper.

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Section: Teleoperated Demonstrationmentioning

confidence: 99%

Robot Learning from Demonstration in Robotic Assembly: A Survey

Zhu

2018

Robotics

186

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“…The team from the Massachusetts Institute of Technology, advised by Brian Williams, also focused on the learning of low-level motion primitives from demonstration. Their approach employed probabilistic flow tubes to infer the desired state region at each time step from the data provided by the demonstrations (Dong and Williams 2010).…”

Section: Learning From Demonstration Challengementioning

confidence: 99%

The AAAI 2011 Robot Exhibition

et al. 2012

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In this article we report on the exhibits and challenges shown at the AAAI 2011 Robotics Program in San Francisco. The event included a broad demonstration of innovative research at the intersection of robotics and artificial intelligence. Through these multi-year challenge events, our goal has been to focus the research community’s energy toward common platforms and common problems to work toward the greater goal of embodied AI.

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“…Therefore, a pre-processing stage over the training datapoints is needed to obtain such type of data. Among the solutions, one can find Dynamic Time Warping (DTW) [25,44] and Hidden Markov Models (HMM) [88,127].…”

Section: Dynamical Systems Modelsmentioning

confidence: 99%

Robot learning from demonstration of force-based manipulation tasks

Rozo

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One of the main challenges in Robotics is to develop robots that can interact with humans in a natural way, sharing the same dynamic and unstructured environments. Such an interaction may be aimed at assisting, helping or collaborating with a human user. To achieve this, the robot must be endowed with a cognitive system that allows it not only to learn new skills from its human partner, but also to refine or improve those already learned. In this context, learning from demonstration appears as a natural and userfriendly way to transfer knowledge from humans to robots. This dissertation addresses such a topic and its application to an unexplored field, namely force-based manipulation tasks learning. In this kind of scenarios, force signals can convey data about the stiffness of a given object, the inertial components acting on a tool, a desired force profile to be reached, etc. Therefore, if the user wants the robot to learn a manipulation skill successfully, it is essential that its cognitive system is able to deal with force perceptions. The first issue this thesis tackles is to extract the input information that is relevant for learning the task at hand, which is also known as the what to imitate? problem. Here, the proposed solution takes into consideration that the robot actions are a function of sensory signals, in other words the importance of each perception is assessed through its correlation with the robot movements. A Mutual Information analysis is used for selecting the most relevant inputs according to their influence on the output space. In this way, the robot can gather all the information coming from its sensory system, and the perception selection module proposed here automatically chooses the data the robot needs to learn a given task. Having selected the relevant input information for the task, it is necessary to represent the human demonstrations in a compact way, encoding the relevant characteristics of the data, for instance, sequential information, uncertainty, constraints, etc. This issue is the next problem addressed in this thesis. Here, a probabilistic learning framework based on hidden Markov models and Gaussian mixture regression is proposed for learning force-based manipulation skills. The outstanding features of such a framework are: (i) it is able to deal with the noise and uncertainty of force signals because of its probabilistic formulation, (ii) it exploits the sequential information embedded in the model for managing perceptual aliasing and time discrepancies, and (iii) it takes advantage of task variables to encode those force-based skills where the robot actions are modulated by an external parameter. Therefore, the resulting learning structure is able to robustly encode and reproduce different manipulation tasks. After, this thesis goes a step forward by proposing a novel whole framework for learning impedance-based behaviors from demonstrations. The key aspects here are that this new structure merges vision and force information for encoding the data compactly, and it allows the robot to have different behaviors by shaping its compliance level over the course of the task. This is achieved by a parametric probabilistic model, whose Gaussian components are the basis of a statistical dynamical system that governs the robot motion. From the force perceptions, the stiffness of the springs composing such a system are estimated, allowing the robot to shape its compliance. This approach permits to extend the learning paradigm to other fields different from the common trajectory following. The proposed frameworks are tested in three scenarios, namely, (a) the ball-in-box task, (b) drink pouring, and (c) a collaborative assembly, where the experimental results evidence the importance of using force perceptions as well as the usefulness and strengths of the methods.

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Motion learning in variable environments using probabilistic flow tubes

Cited by 14 publications

References 15 publications

Robot Learning from Demonstration in Robotic Assembly: A Survey

Robot Learning from Demonstration in Robotic Assembly: A Survey

The AAAI 2011 Robot Exhibition

Robot learning from demonstration of force-based manipulation tasks

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