Search Results for author:
Found 20 papers, 4 papers with code
Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research
One problem with researching cognitive modeling and reinforcement learning (RL) is that researchers spend too much time on setting up an appropriate computational framework for their experiments.
Language-Conditioned Reinforcement Learning to Solve Misunderstandings with Action Corrections
To evaluate our approach, we propose a collection of benchmark environments for action correction in language-conditioned reinforcement learning, utilizing a synthetic instructor to generate language goals and their corresponding corrections.
Intelligent problem-solving as integrated hierarchical reinforcement learning
According to cognitive psychology and related disciplines, the development of complex problem-solving behaviour in biological agents depends on hierarchical cognitive mechanisms.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for Robotics
We show that hindsight instructions improve the learning performance, as expected.
Intelligent behavior depends on the ecological niche: Scaling up AI to human-like intelligence in socio-cultural environments
This paper outlines a perspective on the future of AI, discussing directions for machines models of human-like intelligence.
Hierarchical principles of embodied reinforcement learning: A review
We then relate these insights with contemporary hierarchical reinforcement learning methods, and identify the key machine intelligence approaches that realise these mechanisms.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Sensorimotor representation learning for an "active self" in robots: A model survey
Safe human-robot interactions require robots to be able to learn how to behave appropriately in \sout{humans' world} \rev{spaces populated by people} and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than being provided a rigid set of rules for operations.
Robotic self-representation improves manipulation skills and transfer learning
Cognitive science suggests that the self-representation is critical for learning and problem-solving.
Reinforcement Learning with Time-dependent Goals for Robotic Musicians
Reinforcement learning is a promising method to accomplish robotic control tasks.
Enhancing a Neurocognitive Shared Visuomotor Model for Object Identification, Localization, and Grasping With Learning From Auxiliary Tasks
In this follow-up study, we expand the task and the model to reaching for objects in a three-dimensional space with a novel dataset based on augmented reality and a simulation environment.
Curious Hierarchical Actor-Critic Reinforcement Learning
Hierarchical abstraction and curiosity-driven exploration are two common paradigms in current reinforcement learning approaches to break down difficult problems into a sequence of simpler ones and to overcome reward sparsity.
From semantics to execution: Integrating action planning with reinforcement learning for robotic causal problem-solving
In this article, we build on these novel methods to facilitate the integration of action planning with reinforcement learning by exploiting the reward-sparsity as a bridge between the high-level and low-level state- and control spaces.
Unsupervised Expectation Learning for Multisensory Binding
The model adapts concepts of expectation learning to enhance the unisensory representation based on the learned bindings.
Curriculum goal masking for continuous deep reinforcement learning
Deep reinforcement learning has recently gained a focus on problems where policy or value functions are independent of goals.
Deep Neural Object Analysis by Interactive Auditory Exploration with a Humanoid Robot
We present a novel approach for interactive auditory object analysis with a humanoid robot.
Grounding Dynamic Spatial Relations for Embodied (Robot) Interaction
This paper presents a computational model of the processing of dynamic spatial relations occurring in an embodied robotic interaction setup.
Exploiting Deep Semantics and Compositionality of Natural Language for Human-Robot-Interaction
We develop a natural language interface for human robot interaction that implements reasoning about deep semantics in natural language.
Tractable Epistemic Reasoning with Functional Fluents, Static Causal Laws and Postdiction
We present an epistemic action theory for tractable epistemic reasoning as an extension to the h-approximation (HPX) theory.
Narrative based Postdictive Reasoning for Cognitive Robotics
Making sense of incomplete and conflicting narrative knowledge in the presence of abnormalities, unobservable processes, and other real world considerations is a challenge and crucial requirement for cognitive robotics systems.
h-approximation: History-Based Approximation of Possible World Semantics as ASP
We propose an approximation of the Possible Worlds Semantics (PWS) for action planning.
