ServiceNow Research

Planning

A Planning based Neural-Symbolic Approach for Embodied Instruction Following
The ALFRED environment features an embodied agent following instructions and accomplishing tasks in simulated home environments. …
Scaling up ML-based Black-box Planning with Partial STRIPS Models
A popular approach for sequential decision-making is to perform simulator-based search guided with Machine Learning (ML) methods like …
A Planning based Neural-Symbolic Approach for Embodied Instruction Following
The ALFRED environment features embodied instruction following tasks in simulated home environments. However, end-to-end deep learning …
Scaling up ML-based Black-box Planning with Partial STRIPS Models
A popular approach for sequential decision-making is to perform simulator-based search guided with Machine Learning (ML) methods like …
SEVN: A Sidewalk Simulation Environment for Visual Navigation
Millions of blind and visually-impaired (BVI) people navigate urban environments every day, using smartphones for high-level …
Planning with Latent SImulated Trajectories
In this work, we draw connections between planning and latent variable models1. Specifically, planning can be seen as introducing …
Probabilistic Planning with Sequential Monte Carlo Methods
In this work, we propose a novel formulation of planning which views it as a probabilistic inference problem over future optimal …
Learning Heuristics for the TSP by Policy Gradient
The aim of the study is to provide interesting insights on how efficient machine learning algorithms could be adapted to solve com- …