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Laetitia Matignon

3 accepted papers

2024

Task-Conditioned Adaptation of Visual Features in Multi-Task Policy Learning

CVPR 2024poster

Successfully addressing a wide variety of tasks is a core ability of autonomous agents requiring flexibly adapting the underlying decision-making strategies and as we argue in this work also adapting the perception modules. An analogical argument would be the human visual system which uses top-down…

Cited by 4SourcePDFScholar
2023

Multi-Object Navigation with Dynamically Learned Neural Implicit Representations

ICCV 2023poster

Understanding and mapping a new environment are core abilities of any autonomously navigating agent. While classical robotics usually estimates maps in a stand-alone manner with SLAM variants, which maintain a topological or metric representation, end-to-end learning of navigation keeps some form of…

Cited by 19PDFcodeScholar
2022

Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation

IROS 2022poster

In the context of visual navigation, the capacity to map a novel environment is necessary for an agent to exploit its observation history in the considered place and efficiently reach known goals. This ability can be associated with spatial rea-soning, where an agent is able to perceive spatial rela…

Cited by 29SourcecodeScholar