1 citations · 2 across the 4 of their papers we have counts for
4 papers
Learning Quantitative Automata Modulo Theories
Eric Hsiung, Swarat Chaudhuri, Joydeep Biswas
Quantitative automata are useful representations for numerous applications, including modeling probability distributions over sequences to Markov chains and reward machines. Active…
Automata Learning from Preference and Equivalence Queries
Eric Hsiung, Joydeep Biswas, Swarat Chaudhuri
Active automata learning from membership and equivalence queries is a foundational problem with numerous applications. We propose a novel variant of the active automata learning pr…
Generalizing to New Domains by Mapping Natural Language to Lifted LTL
Eric Hsiung, Hiloni Mehta, Junchi Chu +4
Recent work on using natural language to specify commands to robots has grounded that language to LTL. However, mapping natural language task specifications to LTL task specificati…
Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings
Sreehari Rammohan, Shangqun Yu, Bowen He +4
Learning continuous control in high-dimensional sparse reward settings, such as robotic manipulation, is a challenging problem due to the number of samples often required to obtain…