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Michael H. Lim

2 accepted papers

2022

Multi-Task Learning with Sequence-Conditioned Transporter Networks

ICRA 2022poster

Enabling robots to solve multiple manipulation tasks has a wide range of industrial applications. While learning-based approaches enjoy flexibility and generalizability, scaling these approaches to solve such compositional tasks remains a challenge. In this work, we aim to solve multi-task learning…

Cited by 15SourceScholar
2020

Sparse Tree Search Optimality Guarantees in POMDPs with Continuous Observation Spaces

IJCAI 2020poster

Partially observable Markov decision processes (POMDPs) with continuous state and observation spaces have powerful flexibility for representing real-world decision and control problems but are notoriously difficult to solve. Recent online sampling-based algorithms that use observation likelihood wei…