Search Results for author:
Found 10 papers, 6 papers with code
NeuralThink: Algorithm Synthesis that Extrapolates in General Tasks
To address this gap, we propose NeuralThink, a new recurrent architecture that can consistently extrapolate to both symmetrical and asymmetrical tasks, where the dimensionality of the input and output are different.
Goal-Conditioned Offline Reinforcement Learning via Metric Learning
Experimentally, we show how our method consistently outperforms other offline RL baselines in learning from sub-optimal offline datasets.
Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning
We introduce hybrid execution in multi-agent reinforcement learning (MARL), a new paradigm in which agents aim to successfully complete cooperative tasks with arbitrary communication levels at execution time by taking advantage of information-sharing among the agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Perceive, Represent, Generate: Translating Multimodal Information to Robotic Motion Trajectories
We present Perceive-Represent-Generate (PRG), a novel three-stage framework that maps perceptual information of different modalities (e. g., visual or sound), corresponding to a sequence of instructions, to an adequate sequence of movements to be executed by a robot.
Geometric Multimodal Contrastive Representation Learning
Learning representations of multimodal data that are both informative and robust to missing modalities at test time remains a challenging problem due to the inherent heterogeneity of data obtained from different channels.
How to Sense the World: Leveraging Hierarchy in Multimodal Perception for Robust Reinforcement Learning Agents
This work addresses the problem of sensing the world: how to learn a multimodal representation of a reinforcement learning agent's environment that allows the execution of tasks under incomplete perceptual conditions.
Explainable Agency by Revealing Suboptimality in Child-Robot Learning Scenarios
In the application scenario, the child and the robot learn together how to play a zero-sum game that requires logical and mathematical thinking.
MHVAE: a Human-Inspired Deep Hierarchical Generative Model for Multimodal Representation Learning
Humans are able to create rich representations of their external reality.
Playing Games in the Dark: An approach for cross-modality transfer in reinforcement learning
In this work we explore the use of latent representations obtained from multiple input sensory modalities (such as images or sounds) in allowing an agent to learn and exploit policies over different subsets of input modalities.
Learning multimodal representations for sample-efficient recognition of human actions
In this work we present \textit{motion concepts}, a novel multimodal representation of human actions in a household environment.
