activity
20152023
most citedGeneralized Grounding Graphs: A Probabilistic Framework for Understanding Grounded Commands

23 citations · 51 across the 9 of their papers we have counts for

collaborators
Showing cs.ROShow all

8 papers · 1 filter

cs.RO20217 cited

Grasp and Motion Planning for Dexterous Manipulation for the Real Robot Challenge

Takuma Yoneda, Charles Schaff, Takahiro Maeda +1

This report describes our winning submission to the Real Robot Challenge (https://real-robot-challenge.com/). The Real Robot Challenge is a three-phase dexterous manipulation compe…

cs.RO20201 cited

Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents

Jacopo Tani, Andrea F. Daniele, Gianmarco Bernasconi +10

As robotics matures and increases in complexity, it is more necessary than ever that robot autonomy research be reproducible. Compared to other sciences, there are specific challen…

cs.RO2020

Residual Policy Learning for Shared Autonomy

Charles Schaff, Matthew R. Walter

Shared autonomy provides an effective framework for human-robot collaboration that takes advantage of the complementary strengths of humans and robots to achieve common goals. Many…

cs.RO20192 cited

Language-guided Semantic Mapping and Mobile Manipulation in Partially Observable Environments

Siddharth Patki, Ethan Fahnestock, Thomas M. Howard +1

Recent advances in data-driven models for grounded language understanding have enabled robots to interpret increasingly complex instructions. Two fundamental limitations of these m…

cs.RO2019

Inferring Compact Representations for Efficient Natural Language Understanding of Robot Instructions

Siddharth Patki, Andrea F. Daniele, Matthew R. Walter +1

The speed and accuracy with which robots are able to interpret natural language is fundamental to realizing effective human-robot interaction. A great deal of attention has been pa…

cs.RO2019

The AI Driving Olympics at NeurIPS 2018

Julian Zilly, Jacopo Tani, Breandan Considine +14

Despite recent breakthroughs, the ability of deep learning and reinforcement learning to outperform traditional approaches to control physically embodied robotic agents remains lar…