Multi-modal Feedback for Affordance-driven Interactive Reinforcement Learning
Interactive reinforcement learning (IRL) extends traditional reinforcement learning (RL) by allowing an agent to interact with parentlike trainers during a task. In this paper, we present an IRL approach using dynamic audio-visual input in terms of vocal commands and hand gestures as feedback. Our a...
| Autores principales: | , , |
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| Formato: | Objeto de conferencia Resumen |
| Lenguaje: | Inglés |
| Publicado: |
2018
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| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/70693 http://47jaiio.sadio.org.ar/sites/default/files/ASAI-06.pdf |
| Aporte de: |
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I19-R120-10915-70693 |
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dspace |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Inglés |
| topic |
Ciencias Informáticas interactive reinforcement learning affordances audio-visual feedback parent-like trainer |
| spellingShingle |
Ciencias Informáticas interactive reinforcement learning affordances audio-visual feedback parent-like trainer Cruz, Francisco Parisi, Germán Wermter, Stefan Multi-modal Feedback for Affordance-driven Interactive Reinforcement Learning |
| topic_facet |
Ciencias Informáticas interactive reinforcement learning affordances audio-visual feedback parent-like trainer |
| description |
Interactive reinforcement learning (IRL) extends traditional reinforcement learning (RL) by allowing an agent to interact with parentlike trainers during a task. In this paper, we present an IRL approach using dynamic audio-visual input in terms of vocal commands and hand gestures as feedback. Our architecture integrates multi-modal information to provide robust commands from multiple sensory cues along with a confidence value indicating the trustworthiness of the feedback. The integration process also considers the case in which the two modalities convey incongruent information. Additionally, we modulate the influence of sensory-driven feedback in the IRL task using goal-oriented knowledge in terms of contextual affordances.We implement a neural network architecture to predict the effect of performed actions with different objects to avoid failed-states, i.e., states from which it is not possible to accomplish the task. In our experimental setup, we explore the interplay of multi-modal feedback and task-specific affordances in a robot cleaning scenario. We compare the learning performance of the agent under four different conditions: traditional RL, multi-modal IRL, and each of these two setups with the use of contextual affordances. Our experiments show that the best performance is obtained by using audio-visual feedback with affordance-modulated IRL. The obtained results demonstrate the importance of multi-modal sensory processing integrated with goaloriented knowledge in IRL tasks. |
| format |
Objeto de conferencia Resumen |
| author |
Cruz, Francisco Parisi, Germán Wermter, Stefan |
| author_facet |
Cruz, Francisco Parisi, Germán Wermter, Stefan |
| author_sort |
Cruz, Francisco |
| title |
Multi-modal Feedback for Affordance-driven Interactive Reinforcement Learning |
| title_short |
Multi-modal Feedback for Affordance-driven Interactive Reinforcement Learning |
| title_full |
Multi-modal Feedback for Affordance-driven Interactive Reinforcement Learning |
| title_fullStr |
Multi-modal Feedback for Affordance-driven Interactive Reinforcement Learning |
| title_full_unstemmed |
Multi-modal Feedback for Affordance-driven Interactive Reinforcement Learning |
| title_sort |
multi-modal feedback for affordance-driven interactive reinforcement learning |
| publishDate |
2018 |
| url |
http://sedici.unlp.edu.ar/handle/10915/70693 http://47jaiio.sadio.org.ar/sites/default/files/ASAI-06.pdf |
| work_keys_str_mv |
AT cruzfrancisco multimodalfeedbackforaffordancedriveninteractivereinforcementlearning AT parisigerman multimodalfeedbackforaffordancedriveninteractivereinforcementlearning AT wermterstefan multimodalfeedbackforaffordancedriveninteractivereinforcementlearning |
| bdutipo_str |
Repositorios |
| _version_ |
1764820481693712384 |