Discovering and Manipulating Affordances

Chavéz-García, R. Omar; Andries, Mihai; Luce-Vayrac, Pierre; Chatila, Raja

doi:10.1007/978-3-319-50115-4_59

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…We designed experiments that would confirm the capability of our affordance representation to detect equivalences and non-equivalences between learned affordances. We employed a Bayesian Network structure-learning approach presented in (Chavez-Garcia et al, 2016a ) to describe and learn affordances as relations between random variables (affordance elements). Then we analyse how the learned affordances relate to each case of equivalence presented in Table 2 .…”

Section: Experiments and Results: Affordance Equivalencementioning

confidence: 99%

Affordance Equivalences in Robotics: A Formalism

et al. 2018

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Automatic knowledge grounding is still an open problem in cognitive robotics. Recent research in developmental robotics suggests that a robot's interaction with its environment is a valuable source for collecting such knowledge about the effects of robot's actions. A useful concept for this process is that of an affordance, defined as a relationship between an actor, an action performed by this actor, an object on which the action is performed, and the resulting effect. This paper proposes a formalism for defining and identifying affordance equivalence. By comparing the elements of two affordances, we can identify equivalences between affordances, and thus acquire grounded knowledge for the robot. This is useful when changes occur in the set of actions or objects available to the robot, allowing to find alternative paths to reach goals. In the experimental validation phase we verify if the recorded interaction data is coherent with the identified affordance equivalences. This is done by querying a Bayesian Network that serves as container for the collected interaction data, and verifying that both affordances considered equivalent yield the same effect with a high probability.

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Section: Experiments and Results: Affordance Equivalencementioning

confidence: 99%

Affordance Equivalences in Robotics: A Formalism

et al. 2018

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“…This definition shows an affordance as an acquired relation between the elements in O , A , and E (Chavez-Garcia et al, 2016a ).…”

Section: Perception and Learning Affordancesmentioning

confidence: 99%

“…We validated several pieces of this architecture, using different sets of modules. We employed in an affordance-learning context the combination of modules responsible for Sensorial perception, Motor action execution, and Sensorimotor learning (the 3 yellow modules on the top of the Figure 12 ), previously described in section 2 (Chavez-Garcia et al, 2016b , a ). Similarly, in a human-robot collaboration setting, we employed the modules for Sensorial perception, Motor action execution, Spatial reasoning and knowledge, Knowledge base, Supervision system, Human-aware task planning, Human-aware motion and manipulation planning, Motivation, and Dialogue Manager (Alami, 2013 ; Devin and Alami, 2016 ; Lemaignan et al, 2017 ).…”

Section: Cognitive Architecturementioning

confidence: 99%

Toward Self-Aware Robots

et al. 2018

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Despite major progress in Robotics and AI, robots are still basically "zombies" repeatedly achieving actions and tasks without understanding what they are doing. Deep-Learning AI programs classify tremendous amounts of data without grasping the meaning of their inputs or outputs. We still lack a genuine theory of the underlying principles and methods that would enable robots to understand their environment, to be cognizant of what they do, to take appropriate and timely initiatives, to learn from their own experience and to show that they know that they have learned and how. The rationale of this paper is that the understanding of its environment by an agent (the agent itself and its effects on the environment included) requires its self-awareness, which actually is itself emerging as a result of this understanding and the distinction that the agent is capable to make between its own mind-body and its environment. The paper develops along five issues: agent perception and interaction with the environment; learning actions; agent interaction with other agents-specifically humans; decision-making; and the cognitive architecture integrating these capacities.

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“…Recently, Chavez-Garcia et al [21] proposed the usage of Gaussian Bayesian networks to model the relations between objects' features in continuous space with effects of different actions. The network provides means and covariances of a query variable given the observed variables, and the structure is learned by maximizing Bayesian or Akaike information criterion score.…”

Section: Previous Workmentioning

confidence: 99%

A deep probabilistic framework for heterogeneous self-supervised learning of affordances

Dehban

Jamone

Kampff³

et al. 2017

2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)

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The perception of affordances provides an actioncentered parametric representation of the environment. By perceiving an object's visual features in terms of what actions they afford, novel behavior opportunities can be inferred about previously unseen objects. In this paper, a flexible deep probabilistic framework is proposed which allows an explorative agent to learn tool-object affordances in continuous space. To this end, we use a deep variational auto-encoder with heterogeneous probabilistic distributions to infer the most probable action that achieves a desired effect or to predict a parametric probability distribution over action consequences i.e. effects. Our experiments show the generalization of the method to unseen objects and tools and we have analyzed the influence of different design choices. Our framework goes beyond other proposals by incorporating various probability distributions tailored for each individual modality and by eliminating the need for any pre-processing of the data.

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Discovering and Manipulating Affordances

Cited by 3 publications

References 15 publications

Affordance Equivalences in Robotics: A Formalism

Affordance Equivalences in Robotics: A Formalism

Toward Self-Aware Robots

A deep probabilistic framework for heterogeneous self-supervised learning of affordances

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