114 citations · 195 across the 22 of their papers we have counts for
6 papers · 1 filter
Elucidating Robust Learning with Uncertainty-Aware Corruption Pattern Estimation
Jeongeun Park, Seungyoun Shin, Sangheum Hwang +1
Robust learning methods aim to learn a clean target distribution from noisy and corrupted training data where a specific corruption pattern is often assumed a priori. Our proposed…
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…
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…
Learning Robot Structure and Motion Embeddings using Graph Neural Networks
J. Taery Kim, Jeongeun Park, Sungjoon Choi +1
We propose a learning framework to find the representation of a robot's kinematic structure and motion embedding spaces using graph neural networks (GNN). Finding a compact and low…
Semi-Autonomous Teleoperation via Learning Non-Prehensile Manipulation Skills
Sangbeom Park, Yoonbyung Chai, Sunghyun Park +3
In this paper, we present a semi-autonomous teleoperation framework for a pick-and-place task using an RGB-D sensor. In particular, we assume that the target object is located in a…
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…