17 citations · 84 across the 13 of their papers we have counts for
13 papers
Robotic Manipulation Datasets for Offline Compositional Reinforcement Learning
Marcel Hussing, Jorge A. Mendez, Anisha Singrodia +2
Offline reinforcement learning (RL) is a promising direction that allows RL agents to pre-train on large datasets, avoiding the recurrence of expensive data collection. To advance…
Embodied Lifelong Learning for Task and Motion Planning
Jorge Mendez-Mendez, Leslie Pack Kaelbling, Tomás Lozano-Pérez
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 accumul…
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Megan M. Baker, Alexander New, Mario Aguilar-Simon +44
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks enc…
Lifelong Machine Learning of Functionally Compositional Structures
Jorge A. Mendez
A hallmark of human intelligence is the ability to construct self-contained chunks of knowledge and reuse them in novel combinations for solving different problems. Learning such c…
CompoSuite: A Compositional Reinforcement Learning Benchmark
Jorge A. Mendez, Marcel Hussing, Meghna Gummadi +1
We present CompoSuite, an open-source simulated robotic manipulation benchmark for compositional multi-task reinforcement learning (RL). Each CompoSuite task requires a particular…
How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition
Jorge A. Mendez, Eric Eaton
A major goal of artificial intelligence (AI) is to create an agent capable of acquiring a general understanding of the world. Such an agent would require the ability to continually…