Search Results for author:
Found 7 papers, 2 papers with code
HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers
We analyze the performance of a set of baselines and show a correlation with a real-world evaluation.
Model Predictive Control for Fluid Human-to-Robot Handovers
However, how to responsively generate smooth motions to take an object from a human is still an open question.
Learning Perceptual Concepts by Bootstrapping from Human Queries
Second, we treat this low-dimensional concept as an automatic labeler to synthesize a large-scale high-dimensional data set with the simulator.
Reactive Human-to-Robot Handovers of Arbitrary Objects
We demonstrate the generalizability, usability, and robustness of our approach on a novel benchmark set of 26 diverse household objects, a user study with naive users (N=6) handing over a subset of 15 objects, and a systematic evaluation examining different ways of handing objects.
Human Grasp Classification for Reactive Human-to-Robot Handovers
In this paper, we propose an approach for human-to-robot handovers in which the robot meets the human halfway, by classifying the human's grasp of the object and quickly planning a trajectory accordingly to take the object from the human's hand according to their intent.
Vision-and-Dialog Navigation
To train agents that search an environment for a goal location, we define the Navigation from Dialog History task.
Neural Semantic Parsing with Anonymization for Command Understanding in General-Purpose Service Robots
To address this challenge, we propose an approach that leverages neural semantic parsing methods in combination with contextual word embeddings to enable the training of a semantic parser with little data and without domain specific parser engineering.
