31 citations · 72 across the 22 of their papers we have counts for
4 papers · 1 filter
Flexible and Efficient Long-Range Planning Through Curious Exploration
Aidan Curtis, Minjian Xin, Dilip Arumugam +2
Identifying algorithms that flexibly and efficiently discover temporally-extended multi-phase plans is an essential step for the advancement of robotics and model-based reinforceme…
A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions
Siddharth Karamcheti, Edward C. Williams, Dilip Arumugam +4
Robots operating alongside humans in diverse, stochastic environments must be able to accurately interpret natural language commands. These instructions often fall into one of two…
Modeling Latent Attention Within Neural Networks
Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti +3
Deep neural networks are able to solve tasks across a variety of domains and modalities of data. Despite many empirical successes, we lack the ability to clearly understand and int…
Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities
Dilip Arumugam, Siddharth Karamcheti, Nakul Gopalan +2
Humans can ground natural language commands to tasks at both abstract and fine-grained levels of specificity. For instance, a human forklift operator can be instructed to perform a…