8 papers
PrefPI: Preference-Guided Steering into Out-of-Distribution Behaviors
Seungeun Rho, Wontaek Kim, Danfei Xu +1
We present PrefPI (Preference-Guided Policy Iteration), an iterative framework for steering pretrained generative robot policies using only relative preferences over self-generated…
Game-Guided Skill Discovery through Self-Play for Playable Agent Control
Seungeun Rho, Jeonghwan Kim, Xue Bin Peng +1
We present Game-Guided Skill Discovery (GGSD), a framework that uses self-play in games to discover motor skills that are directly playable by humans. Playable skills provide a com…
LineRides: Line-Guided Reinforcement Learning for Bicycle Robot Stunts
Seungeun Rho, Shamel Fahmi, Jeonghwan Kim +3
Designing reward functions for agile robotic maneuvers in reinforcement learning remains difficult, and demonstration-based approaches often require reference motions that are unav…
Flip Stunts on Bicycle Robots using Iterative Motion Imitation
Jeonghwan Kim, Shamel Fahmi, Seungeun Rho +2
This work demonstrates a front-flip on bicycle robots via reinforcement learning, particularly by imitating reference motions that are infeasible and imperfect. To address this, we…
Reference Grounded Skill Discovery
Seungeun Rho, Aaron Trinh, Danfei Xu +1
Scaling unsupervised skill discovery algorithms to high-DoF agents remains challenging. As dimensionality increases, the exploration space grows exponentially, while the manifold o…
Switch4EAI: Leveraging Console Game Platform for Benchmarking Robotic Athletics
Tianyu Li, Jeonghwan Kim, Wontaek Kim +3
Recent advances in whole-body robot control have enabled humanoid and legged robots to execute increasingly agile and coordinated movements. However, standardized benchmarks for ev…
Unsupervised Skill Discovery as Exploration for Learning Agile Locomotion
Seungeun Rho, Kartik Garg, Morgan Byrd +1
Exploration is crucial for enabling legged robots to learn agile locomotion behaviors that can overcome diverse obstacles. However, such exploration is inherently challenging, and…
Language Guided Skill Discovery
Seungeun Rho, Laura Smith, Tianyu Li +3
Skill discovery methods enable agents to learn diverse emergent behaviors without explicit rewards. To make learned skills useful for unknown downstream tasks, obtaining a semantic…