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Found 7 papers, 4 papers with code
Learning Neuro-symbolic Programs for Language Guided Robot Manipulation
Given a natural language instruction and an input scene, our goal is to train a model to output a manipulation program that can be executed by the robot.
GoalNet: Inferring Conjunctive Goal Predicates from Human Plan Demonstrations for Robot Instruction Following
Our goal is to enable a robot to learn how to sequence its actions to perform tasks specified as natural language instructions, given successful demonstrations from a human partner.
TANGO: Commonsense Generalization in Predicting Tool Interactions for Mobile Manipulators
We introduce a novel neural model, termed TANGO, for predicting task-specific tool interactions, trained using demonstrations from human teachers instructing a virtual robot.
Multi-facet Universal Schema
To address the violation of the USchema assumption, we propose multi-facet universal schema that uses a neural model to represent each sentence pattern as multiple facet embeddings and encourage one of these facet embeddings to be close to that of another sentence pattern if they co-occur with the same entity pair.
Open source software for automatic subregional assessment of knee cartilage degradation using quantitative T2 relaxometry and deep learning
Subregional T2 values and four-year changes were calculated using a musculoskeletal radiologist's segmentations (Reader 1) and the model's segmentations.
ToolNet: Using Commonsense Generalization for Predicting Tool Use for Robot Plan Synthesis
When compared to a graph neural network baseline, it achieves 14-27% accuracy improvement for predicting known tools from new world scenes, and 44-67% improvement in generalization for novel objects not encountered during training.
Robotics
Leveraging Past References for Robust Language Grounding
We therefore reframe the grounding problem from the perspective of coreference detection and propose a neural network that detects when two expressions are referring to the same object.
