activity
20172022
most citedTsallis Reinforcement Learning: A Unified Framework for Maximum Entropy Reinforcement Learning

19 citations · 21 across the 7 of their papers we have counts for

collaborators
Showing cs.ROShow all

5 papers · 1 filter

cs.RO2021

Semi-Supervised Imitation Learning with Mixed Qualities of Demonstrations for Autonomous Driving

Gunmin Lee, Wooseok Oh, Seungyoun Shin +5

In this paper, we consider the problem of autonomous driving using imitation learning in a semi-supervised manner. In particular, both labeled and unlabeled demonstrations are leve…

cs.RO2021

Towards Defensive Autonomous Driving: Collecting and Probing Driving Demonstrations of Mixed Qualities

Jeongwoo Oh, Gunmin Lee, Jeongeun Park +8

Designing or learning an autonomous driving policy is undoubtedly a challenging task as the policy has to maintain its safety in all corner cases. In order to secure safety in auto…

cs.RO2021

Self-Supervised Motion Retargeting with Safety Guarantee

Sungjoon Choi, Min Jae Song, Hyemin Ahn +1

In this paper, we present self-supervised shared latent embedding (S3LE), a data-driven motion retargeting method that enables the generation of natural motions in humanoid robots…

cs.RO2018

Interactive Text2Pickup Network for Natural Language based Human-Robot Collaboration

Hyemin Ahn, Sungjoon Choi, Nuri Kim +2

In this paper, we propose the Interactive Text2Pickup (IT2P) network for human-robot collaboration which enables an effective interaction with a human user despite the ambiguity in…

cs.RO2017

A Nonparametric Motion Flow Model for Human Robot Cooperation

Sungjoon Choi, Kyungjae Lee, H. Andy Park +1

In this paper, we present a novel nonparametric motion flow model that effectively describes a motion trajectory of a human and its application to human robot cooperation. To this…