Jorge Mendez-Mendez — accepted AI-conference papers

2026

Position: Modular Memory is the Key to Continual Learning Agents

Vaggelis Dorovatas, Malte Schwerin, Andrew Bagdanov, Lucas Caccia, Antonio Carta, Laurent Charlin +18

ICML 2026spotlight

Foundation models have transformed machine learning through large-scale pretraining, massive parameterization, and increased test-time compute. Despite surpassing human performance in several domains, these models remain fundamentally limited in continuous operation, experience accumulation, and per…

Cited by 0 Source ↗Scholar ↗

2024

Learning Long-Horizon Action Dependencies in Sampling-Based Bilevel Planning

Bartłomiej Cieślar, Leslie Pack Kaelbling, Tomás Lozano-Pérez, Jorge Mendez-Mendez

CoRL 2024poster

Autonomous robots will need the ability to make task and motion plans that involve long sequences of actions, e.g. to prepare a meal. One challenge is that the feasibility of actions late in the plan may depend on much earlier actions. This issue is exacerbated if these dependencies exist at a purel…

Cited by 0 Source ↗Scholar ↗

2023

Embodied Lifelong Learning for Task and Motion Planning

Jorge Mendez-Mendez, Leslie Pack Kaelbling, Tomás Lozano-Pérez

CoRL 2023poster

A robot deployed in a home over long stretches of time faces a true lifelong learning problem. As it seeks to provide assistance to its users, the robot should leverage any accumulated experience to improve its own knowledge and proficiency. We formalize this setting with a novel formulation of life…

Cited by 20 Source ↗Scholar ↗