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Doohyun Lee

2 accepted papers

2025

Subtask-Aware Visual Reward Learning from Segmented Demonstrations

ICLR 2025poster

Reinforcement Learning (RL) agents have demonstrated their potential across various robotic tasks. However, they still heavily rely on human-engineered reward functions, requiring extensive trial-and-error and access to target behavior information, often unavailable in real-world settings. This pape…

Cited by 0SourcePDFScholar
2023

FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation

RSS 2023poster

Reinforcement learning (RL), imitation learning (IL), and task and motion planning (TAMP) have demonstrated impressive performance across various robotic manipulation tasks. However, these approaches have been limited to learning simple behaviors in current real-world manipulation benchmarks, such a…