Search Results for author:
Found 5 papers, 4 papers with code
Few-shot Object Grounding and Mapping for Natural Language Robot Instruction Following
We study the problem of learning a robot policy to follow natural language instructions that can be easily extended to reason about new objects.
An Information-Theoretic Approach to Persistent Environment Monitoring Through Low Rank Model Based Planning and Prediction
We combine a low rank model of a target attribute with an information-maximizing path planner to predict the state of the attribute throughout a region.
Learning to Map Natural Language Instructions to Physical Quadcopter Control using Simulated Flight
Learning uses both simulation and real environments without requiring autonomous flight in the physical environment during training, and combines supervised learning for predicting positions to visit and reinforcement learning for continuous control.
Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation Prediction
We propose an approach for mapping natural language instructions and raw observations to continuous control of a quadcopter drone.
Following High-level Navigation Instructions on a Simulated Quadcopter with Imitation Learning
We introduce a method for following high-level navigation instructions by mapping directly from images, instructions and pose estimates to continuous low-level velocity commands for real-time control.
