Search Results for author:
Found 26 papers, 7 papers with code
Diffusing in Someone Else's Shoes: Robotic Perspective Taking with Diffusion
Being able to mentally transfer from a demonstration seen from a third-person perspective to how it should look from a first-person perspective is fundamental for this ability in humans.
Robotic Imitation of Human Actions
Imitation can allow us to quickly gain an understanding of a new task.
CycleIK: Neuro-inspired Inverse Kinematics
The paper introduces CycleIK, a neuro-robotic approach that wraps two novel neuro-inspired methods for the inverse kinematics (IK) task, a Generative Adversarial Network (GAN), and a Multi-Layer Perceptron architecture.
Clarifying the Half Full or Half Empty Question: Multimodal Container Classification
Multimodal integration is a key component of allowing robots to perceive the world.
Learning Bidirectional Action-Language Translation with Limited Supervision and Incongruent Input
Only occasionally, a learning infant would receive a matching verbal description of an action it is committing, which is similar to supervised learning.
Neuro-Symbolic Spatio-Temporal Reasoning
In this chapter, we view this integration problem from the perspective of Neuro-Symbolic AI.
Learning to Autonomously Reach Objects with NICO and Grow-When-Required Networks
The act of reaching for an object is a fundamental yet complex skill for a robotic agent, requiring a high degree of visuomotor control and coordination.
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
Learning Flexible Translation between Robot Actions and Language Descriptions
In this work, we propose the paired gated autoencoders (PGAE) for flexible translation between robot actions and language descriptions in a tabletop object manipulation scenario.
Knowing Earlier what Right Means to You: A Comprehensive VQA Dataset for Grounding Relative Directions via Multi-Task Learning
Spatial reasoning poses a particular challenge for intelligent agents and is at the same time a prerequisite for their successful interaction and communication in the physical world.
What is Right for Me is Not Yet Right for You: A Dataset for Grounding Relative Directions via Multi-Task Learning
Grounding relative directions is more difficult than grounding absolute directions because it not only requires a model to detect objects in the image and to identify spatial relation based on this information, but it also needs to recognize the orientation of objects and integrate this information into the reasoning process.
Language Model-Based Paired Variational Autoencoders for Robotic Language Learning
Human infants learn language while interacting with their environment in which their caregivers may describe the objects and actions they perform.
A trained humanoid robot can perform human-like crossmodal social attention and conflict resolution
Our saliency prediction model was trained to detect social cues, predict audio-visual saliency, and attend selectively for the robot study.
Model Mediated Teleoperation with a Hand-Arm Exoskeleton in Long Time Delays Using Reinforcement Learning
Telerobotic systems must adapt to new environmental conditions and deal with high uncertainty caused by long-time delays.
Continual Learning from Synthetic Data for a Humanoid Exercise Robot
In a practical scenario, a physical exercise is performed by an expert like a physiotherapist and then used as a reference for a humanoid robot like Pepper to give feedback on a patient's execution of the same exercise.
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
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.
Crossmodal Language Grounding in an Embodied Neurocognitive Model
From a neuroscientific perspective, natural language is embodied, grounded in most, if not all, sensory and sensorimotor modalities, and acquired by means of crossmodal integration.
Explainable Goal-Driven Agents and Robots -- A Comprehensive Review
The recent stance on the explainability of AI systems has witnessed several approaches on eXplainable Artificial Intelligence (XAI); however, most of the studies have focused on data-driven XAI systems applied in computational sciences.
Improving Robot Dual-System Motor Learning with Intrinsically Motivated Meta-Control and Latent-Space Experience Imagination
In this paper, we present a novel dual-system motor learning approach where a meta-controller arbitrates online between model-based and model-free decisions based on an estimate of the local reliability of the learned model.
Solving Visual Object Ambiguities when Pointing: An Unsupervised Learning Approach
The implementation of an intuitive gesture scenario is still challenging because both the pointing intention and the corresponding object have to be correctly recognized in real-time.
Efficient Intrinsically Motivated Robotic Grasping with Learning-Adaptive Imagination in Latent Space
The learned models are used to generate imagined experiences, augmenting the training set of real experiences.
What can computational models learn from human selective attention? A review from an audiovisual crossmodal perspective
Selective attention plays an essential role in information acquisition and utilization from the environment.
Curious Meta-Controller: Adaptive Alternation between Model-Based and Model-Free Control in Deep Reinforcement Learning
Recent success in deep reinforcement learning for continuous control has been dominated by model-free approaches which, unlike model-based approaches, do not suffer from representational limitations in making assumptions about the world dynamics and model errors inevitable in complex domains.
Deep Intrinsically Motivated Continuous Actor-Critic for Efficient Robotic Visuomotor Skill Learning
In this paper, we present a new intrinsically motivated actor-critic algorithm for learning continuous motor skills directly from raw visual input.
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.
