Search Results for author:
Found 8 papers, 1 papers with code
Following the Human Thread in Social Navigation
We propose the first Social Dynamics Adaptation model (SDA) based on the robot's state-action history to infer the social dynamics.
Simple and Effective Transfer Learning for Neuro-Symbolic Integration
Then, a NeSy model is trained on the same task via transfer learning, where the weights of the perceptual part are injected from the pretrained network.
Exploiting Proximity-Aware Tasks for Embodied Social Navigation
In this paper, we propose an end-to-end architecture that exploits Proximity-Aware Tasks (referred as to Risk and Proximity Compass) to inject into a reinforcement learning navigation policy the ability to infer common-sense social behaviors.
Retrospectives on the Embodied AI Workshop
We present a retrospective on the state of Embodied AI research.
Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions
In this paper, we propose Deep Symbolic Learning (DSL), a NeSy system that learns NeSy-functions, i. e., the composition of a (set of) perception functions which map continuous data to discrete symbols, and a symbolic function over the set of symbols.
Online Learning of Reusable Abstract Models for Object Goal Navigation
In this paper, we present a novel approach to incrementally learn an Abstract Model of an unknown environment, and show how an agent can reuse the learned model for tackling the Object Goal Navigation task.
Exploiting Scene-specific Features for Object Goal Navigation
We study this question in the context of Object Navigation, a problem in which an agent has to reach an object of a specific class while moving in a complex domestic environment.
