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Found 16 papers, 2 papers with code
Verifiably Following Complex Robot Instructions with Foundation Models
We propose Language Instruction grounding for Motion Planning (LIMP), a system that leverages foundation models and temporal logics to generate instruction-conditioned semantic maps that enable robots to verifiably follow expressive and long-horizon instructions with open vocabulary referents and complex spatiotemporal constraints.
Language-Conditioned Observation Models for Visual Object Search
In this work, we bridge the gap in realistic object search by posing the search problem as a partially observable Markov decision process (POMDP) where the object detector and visual sensor noise in the observation model is determined by a single Deep Neural Network conditioned on complex language descriptions.
A Virtual Reality Teleoperation Interface for Industrial Robot Manipulators
We address the problem of teleoperating an industrial robot manipulator via a commercially available Virtual Reality (VR) interface.
CAPE: Corrective Actions from Precondition Errors using Large Language Models
Our improvements transfer to a Boston Dynamics Spot robot initialized with a set of skills (specified in language) and associated preconditions, where CAPE improves the correctness metric of the executed task plans by 76. 49% compared to SayCan.
Structural Biases for Improving Transformers on Translation into Morphologically Rich Languages
Machine translation has seen rapid progress with the advent of Transformer-based models.
CHARM: A Hierarchical Deep Learning Model for Classification of Complex Human Activities Using Motion Sensors
In this paper, we report a hierarchical deep learning model for classification of complex human activities using motion sensors.
Learning robot motor skills with mixed reality
Mixed Reality (MR) has recently shown great success as an intuitive interface for enabling end-users to teach robots.
Scalable knowledge base completion with superposition memories
We present Harmonic Memory Networks (HMem), a neural architecture for knowledge base completion that models entities as weighted sums of pairwise bindings between an entity's neighbors and corresponding relations.
Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings
Learning continuous control in high-dimensional sparse reward settings, such as robotic manipulation, is a challenging problem due to the number of samples often required to obtain accurate optimal value and policy estimates.
Compositional Processing Emerges in Neural Networks Solving Math Problems
A longstanding question in cognitive science concerns the learning mechanisms underlying compositionality in human cognition.
Bootstrapping Motor Skill Learning with Motion Planning
We propose using kinematic motion planning as a completely autonomous, sample efficient way to bootstrap motor skill learning for object manipulation.
Geometrical morphology
We explore inflectional morphology as an example of the relationship of the discrete and the continuous in linguistics.
