2 citations · 3 across the 4 of their papers we have counts for
6 papers
Efficient Exploration via First-Person Behavior Cloning Assisted Rapidly-Exploring Random Trees
Max Zuo, Logan Schick, Matthew Gombolay +1
Modern day computer games have extremely large state and action spaces. To detect bugs in these games' models, human testers play the games repeatedly to explore the game and find…
Learning to Follow Language Instructions with Compositional Policies
Vanya Cohen, Geraud Nangue Tasse, Nakul Gopalan +3
We propose a framework that learns to execute natural language instructions in an environment consisting of goal-reaching tasks that share components of their task descriptions. Ou…
Robot Object Retrieval with Contextual Natural Language Queries
Thao Nguyen, Nakul Gopalan, Roma Patel +3
Natural language object retrieval is a highly useful yet challenging task for robots in human-centric environments. Previous work has primarily focused on commands specifying the d…
Grounding Language Attributes to Objects using Bayesian Eigenobjects
Vanya Cohen, Benjamin Burchfiel, Thao Nguyen +3
We develop a system to disambiguate object instances within the same class based on simple physical descriptions. The system takes as input a natural language phrase and a depth im…
Mitigating Planner Overfitting in Model-Based Reinforcement Learning
Dilip Arumugam, David Abel, Kavosh Asadi +5
An agent with an inaccurate model of its environment faces a difficult choice: it can ignore the errors in its model and act in the real world in whatever way it determines is opti…
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…