Discovering affordances through perception and manipulation

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

doi:10.1109/iros.2016.7759583

Cited by 7 publications

(8 citation statements)

References 20 publications

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“…We implemented an information-compression score to estimate how well a Bayesian Network structure describes data

(Chavez-Garcia et al, 2016b ). Our score is based on the Minimum Description Length (MDL) score:…”

Section: Experiments and Results: Affordance Equivalencementioning

confidence: 99%

“…In this paper we employed the graphical model approach for learning affordances proposed by Montesano et al ( 2008 ). In addition, we rely purely on perception-interaction data, without using a priori information (Chavez-Garcia et al, 2016b ). To facilitate the experimental setup, we used pre-defined sensorial and motor capabilities for our robots.…”

Section: Introductionmentioning

confidence: 99%

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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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“…We implemented an information-compression score to estimate how well a Bayesian Network structure describes data

(Chavez-Garcia et al, 2016b ). Our score is based on the Minimum Description Length (MDL) score:…”

Section: Experiments and Results: Affordance Equivalencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Affordance Equivalences in Robotics: A Formalism

et al. 2018

Self Cite

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“…In [12], Bayesian Networks are used to learn dependencies between actions, effects, and visual properties; a strongly spread definition of affordances in robotics. Likewise, [19] also uses a Bayesian approach but coupled with a fixed and finite pre-programmed action set to learn affordances. In our case, we aim to continual learning of multiple affordances through the interaction with its environment.…”

Section: Affordances Learningmentioning

confidence: 99%

Hierarchical Affordance Discovery using Intrinsic Motivation

Manoury

Nguyen

Buche

2019

Proceedings of the 7th International Conference on Human-Agent Interaction

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To be capable of lifelong learning in a real-life environment, robots have to tackle multiple challenges. Being able to relate physical properties they may observe in their environment to possible interactions they may have is one of them. This skill, named affordance learning, is strongly related to embodiment and is mastered through each person's development: each individual learns affordances differently through their own interactions with their surroundings. Current methods for affordance learning usually use either fixed actions to learn these affordances or focus on static setups involving a robotic arm to be operated. In this article, we propose an algorithm using intrinsic motivation to guide the learning of affordances for a mobile robot. This algorithm is capable to autonomously discover, learn and adapt interrelated affordances without pre-programmed actions. Once learned, these affordances may be used by the algorithm to plan sequences of actions in order to perform tasks of various difficulties. We then present one experiment and analyse our system before comparing it with other approaches from reinforcement learning and affordance learning.

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“…Dearden and Demiris (2005), Demiris and Dearden (2005), and Hart et al (2005) are the first works to propose representing the forward and inverse models using Bayesian Networks (BN) in this context, used to play imitation games. Inspired by the previous works, Lopes et al (2007), Montesano et al (2008), Osório et al (2010), and Chavez-Garcia et al (2016) define an affordance as a BN representing the relation between action, object and effect. They provide built-in grasp, tap, and touch actions to also play imitation games.…”

Section: Related Workmentioning

confidence: 99%

Action Generation Adapted to Low-Level and High-Level Robot-Object Interaction States

Maestre¹,

Mukhtar²,

Gonzales³

et al. 2019

Front. Neurorobot.

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Our daily environments are complex, composed of objects with different features. These features can be categorized into low-level features, e.g., an object position or temperature, and high-level features resulting from a pre-processing of low-level features for decision purposes, e.g., a binary value saying if it is too hot to be grasped. Besides, our environments are dynamic, i.e., object states can change at any moment. Therefore, robots performing tasks in these environments must have the capacity to (i) identify the next action to execute based on the available low-level and high-level object states, and (ii) dynamically adapt their actions to state changes. We introduce a method named Interaction State-based Skill Learning (IS 2 L), which builds skills to solve tasks in realistic environments. A skill is a Bayesian Network that infers actions composed of a sequence of movements of the robot's end-effector, which locally adapt to spatio-temporal perturbations using a dynamical system. In the current paper, an external agent performs one or more kinesthetic demonstrations of an action generating a dataset of high-level and low-level states of the robot and the environment objects. First, the method transforms each interaction to represent (i) the relationship between the robot and the object and (ii) the next robot end-effector movement to perform at consecutive instants of time. Then, the skill is built, i.e., the Bayesian network is learned. While generating an action this skill relies on the robot and object states to infer the next movement to execute. This movement selection gets inspired by a type of predictive models for action selection usually called affordances. The main contribution of this paper is combining the main features of dynamical systems and affordances in a unique method to build skills that solve tasks in realistic scenarios. More precisely, combining the low-level movement generation of the dynamical systems, to adapt to local perturbations, with the next movement selection simultaneously based on high-level and low-level states. This contribution was assessed in three experiments in realistic environments using both high-level and low-level states. The built skills solved the respective tasks relying on both types of states, and adapting to external perturbations.

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Discovering affordances through perception and manipulation

Cited by 7 publications

References 20 publications

Affordance Equivalences in Robotics: A Formalism

Affordance Equivalences in Robotics: A Formalism

Hierarchical Affordance Discovery using Intrinsic Motivation

Action Generation Adapted to Low-Level and High-Level Robot-Object Interaction States

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