Simultaneous recognition and reproduction of demonstrated behavior

Billing, Erik; Hellström, Thomas; Janlert, Lars-Erik

doi:10.1016/j.bica.2015.03.002

Cited by 6 publications

(15 citation statements)

References 40 publications

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“…In the present work, we combine previous efforts on internal simulation (Stening et al, 2005;Ziemke et al, 2005; with those on Learning from Demonstration (LFD) (Billing, 2012) into a model that can learn from human demonstrations and reenact the demonstrated behavior both overtly and covertly. We here evaluate several aspects of such covert action: (1) can the robot produce internal simulations similar to a previously executed overt behavior; (2) to what extent can the system produce internal simulations of new behavior, that is, reenact and recombine previously experienced episodes into a novel path through the environment; and (3) how can such internal simulations in a robot be compared with simulation theories of human cognition?…”

Section: Introductionmentioning

confidence: 99%

“…This definition of confidence has its roots in the MOSAIC architecture (Haruno et al, 2003) and has previously been used to compute the responsibility signal λt = γt online (Billing et al, 2015).…”

Section: Overt and Covert Actionsmentioning

confidence: 99%

“…Seen as a directed graph between sensory-motor events, PSL implements several aspects of a joint procedural-episodic memory (Vernon et al, 2015). For a detailed description of the learning process of PSL, please refer to Billing et al (2015).…”

Section: Predictive Sequence Learningmentioning

confidence: 99%

“…While λ is in general time varying and can be computed dynamically, using a method for behavior recognition (Billing et al, 2011b(Billing et al, , 2015, it was here used as a constant goal signal.…”

Section: Overt and Covert Actionsmentioning

confidence: 99%

“…Billing et al (2015) employed a center of max defuzzification method, while we here use a probabilistic approach.Ê is treated as a probability distribution and converted to crisp values by randomly selecting a predicted sensory-motor eventê ∈Ê in proportion to their membership inÊ.…”

Section: Matching Hypothesesmentioning

confidence: 99%

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Finding Your Way from the Bed to the Kitchen: Reenacting and Recombining Sensorimotor Episodes Learned from Human Demonstration

et al. 2016

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Several simulation theories have been proposed as an explanation for how humans and other agents internalize an "inner world" that allows them to simulate interactions with the external real world -prospectively and retrospectively. Such internal simulation of interaction with the environment has been argued to be a key mechanism behind mentalizing and planning. In the present work, we study internal simulations in a robot acting in a simulated human environment. A model of sensory-motor interactions with the environment is generated from human demonstrations and tested on a Robosoft Kompaï robot. The model is used as a controller for the robot, reproducing the demonstrated behavior. Information from several different demonstrations is mixed, allowing the robot to produce novel paths through the environment, toward a goal specified by top-down contextual information. The robot model is also used in a covert mode, where the execution of actions is inhibited and perceptions are generated by a forward model. As a result, the robot generates an internal simulation of the sensory-motor interactions with the environment. Similar to the overt mode, the model is able to reproduce the demonstrated behavior as internal simulations. When experiences from several demonstrations are combined with a top-down goal signal, the system produces internal simulations of novel paths through the environment. These results can be understood as the robot imagining an "inner world" generated from previous experience, allowing it to try out different possible futures without executing actions overtly. We found that the success rate in terms of reaching the specified goal was higher during internal simulation, compared to overt action. These results are linked to a reduction in prediction errors generated during covert action. Despite the fact that the model is quite successful in terms of generating covert behavior toward specified goals, internal simulations display different temporal distributions compared to their overt counterparts. Links to human cognition and specifically mental imagery are discussed.

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Section: Introductionmentioning

confidence: 99%

Section: Overt and Covert Actionsmentioning

confidence: 99%

Section: Predictive Sequence Learningmentioning

confidence: 99%

Section: Overt and Covert Actionsmentioning

confidence: 99%

Section: Matching Hypothesesmentioning

confidence: 99%

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Finding Your Way from the Bed to the Kitchen: Reenacting and Recombining Sensorimotor Episodes Learned from Human Demonstration

et al. 2016

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Simultaneous planning and action: neural-dynamic sequencing of elementary behaviors in robot navigation

2015

Self Cite

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Here, we demonstrate how chains of competing attractors can be used to represent dynamic plans towards a goal state.The present work can be seen as an addition to a growing body of work that demonstrates the role of DFT as a bridge between low-level reactive approaches and high-level symbol processing mechanisms. The architecture is evaluated on a set of planning problems using a simulated e-puck robot, including analysis of the system's behavior in response to noise and temporary blockages of the planned route. The system makes no explicit distinction between planning and execution (in terms of neural activation). This allows continuous adaptation of the planned path. The proposed architecture exploits the DFT property of stability in relation to noise and changes in the environment. The neural dynamics are also exploited such that stay-or-switch action selection emerges where blockage of a planned path occurs: stay until the transient blockage is removed versus switch and replan to an alternative route to the goal.

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Toward navigation ability for autonomous mobile robots with learning from demonstration paradigm

Zhang

Zhong

2018

International Journal of Advanced Robotic Systems

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Learning from demonstration, as an important component of imitation learning, is a paradigm for robot to learn new tasks. Considering the application of learning from demonstration in the navigation issue, the robot can also acquire the navigation task via the human teacher's demonstration. Based on research of the human brain neocortex, in this article, we present a learning from demonstration navigation paradigm from the perspective of hierarchical temporal memory theory. As a type of end-to-end learning form, the demonstrated relationship between perception data and motion commands will be learned and predicted by using hierarchical temporal memory. This framework first perceives images to obtain the corresponding categories information; then the categories incorporated with depth and motion command data are encoded as a sequence of sparse distributed representation vectors. The sequential vectors are treated as the inputs to train the navigation hierarchical temporal memory. After the training, the navigation hierarchical temporal memory stores the transitions of the perceived images, depth, and motion data so that future motion commands can be predicted. The performance of the proposed navigation strategy is evaluated via the real experiments and the public data sets.

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Simultaneous recognition and reproduction of demonstrated behavior

Cited by 6 publications

References 40 publications

Finding Your Way from the Bed to the Kitchen: Reenacting and Recombining Sensorimotor Episodes Learned from Human Demonstration

Finding Your Way from the Bed to the Kitchen: Reenacting and Recombining Sensorimotor Episodes Learned from Human Demonstration

Simultaneous planning and action: neural-dynamic sequencing of elementary behaviors in robot navigation

Toward navigation ability for autonomous mobile robots with learning from demonstration paradigm

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