25 citations · 58 across the 23 of their papers we have counts for
6 papers · 1 filter
Diffused Task-Agnostic Milestone Planner
Mineui Hong, Minjae Kang, Songhwai Oh
Addressing decision-making problems using sequence modeling to predict future trajectories shows promising results in recent years. In this paper, we take a step further to leverag…
TRC: Trust Region Conditional Value at Risk for Safe Reinforcement Learning
Dohyeong Kim, Songhwai Oh
As safety is of paramount importance in robotics, reinforcement learning that reflects safety, called safe RL, has been studied extensively. In safe RL, we aim to find a policy whi…
Efficient Off-Policy Safe Reinforcement Learning Using Trust Region Conditional Value at Risk
Dohyeong Kim, Songhwai Oh
This paper aims to solve a safe reinforcement learning (RL) problem with risk measure-based constraints. As risk measures, such as conditional value at risk (CVaR), focus on the ta…
Renderable Neural Radiance Map for Visual Navigation
Obin Kwon, Jeongho Park, Songhwai Oh
We propose a novel type of map for visual navigation, a renderable neural radiance map (RNR-Map), which is designed to contain the overall visual information of a 3D environment. T…
Meta-Explore: Exploratory Hierarchical Vision-and-Language Navigation Using Scene Object Spectrum Grounding
Minyoung Hwang, Jaeyeon Jeong, Minsoo Kim +2
The main challenge in vision-and-language navigation (VLN) is how to understand natural-language instructions in an unseen environment. The main limitation of conventional VLN algo…
Trust Region-Based Safe Distributional Reinforcement Learning for Multiple Constraints
Dohyeong Kim, Kyungjae Lee, Songhwai Oh
In safety-critical robotic tasks, potential failures must be reduced, and multiple constraints must be met, such as avoiding collisions, limiting energy consumption, and maintainin…