113 citations · 319 across the 38 of their papers we have counts for
7 papers · 1 filter
Generalized Grounding Graphs: A Probabilistic Framework for Understanding Grounded Commands
Thomas Kollar, Stefanie Tellex, Matthew Walter +8
Many task domains require robots to interpret and act upon natural language commands which are given by people and which refer to the robot's physical surroundings. Such interpreta…
Implementing the Deep Q-Network
Melrose Roderick, James MacGlashan, Stefanie Tellex
The Deep Q-Network proposed by Mnih et al. [2015] has become a benchmark and building point for much deep reinforcement learning research. However, replicating results for complex…
Deep Abstract Q-Networks
Melrose Roderick, Christopher Grimm, Stefanie Tellex
We examine the problem of learning and planning on high-dimensional domains with long horizons and sparse rewards. Recent approaches have shown great successes in many Atari 2600 d…
Communicating Robot Arm Motion Intent Through Mixed Reality Head-mounted Displays
Eric Rosen, David Whitney, Elizabeth Phillips +4
Efficient motion intent communication is necessary for safe and collaborative work environments with collocated humans and robots. Humans efficiently communicate their motion inten…
Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning
Lucas Lehnert, Stefanie Tellex, Michael L. Littman
One question central to Reinforcement Learning is how to learn a feature representation that supports algorithm scaling and re-use of learned information from different tasks. Succ…
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…