1 citations · 1 across the 7 of their papers we have counts for
9 papers
PopGen: Generating Semantically Diverse Populations for Zero-Shot Coordination
Paul Teiletche, Kanghyun Ryu, Kyungjoon Park +3
In multi-agent reinforcement learning, exposing a cooperator to partners with diverse strategies during training is key to better collaboration at test time. However, existing meth…
HuGo: LLMs as Whole-Body Policy Code Designers for Humanoid Loco-Manipulation
Seoyeon Choi, Shizhao Ye, Nicholas Bui +5
For humanoids to be useful in everyday environments, they must perform a wide range of tasks that couple locomotion and manipulation. Existing approaches commonly acquire a loco-ma…
CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks
Seoyeon Choi, Kanghyun Ryu, Jonghoon Ock +1
Multi-Agent Reinforcement Learning (MARL) provides a powerful framework for learning coordination in multi-agent systems. However, applying MARL to robotics remains challenging due…
IANN-MPPI: Interaction-Aware Neural Network-Enhanced Model Predictive Path Integral Approach for Autonomous Driving
Kanghyun Ryu, Minjun Sung, Piyush Gupta +4
Motion planning for autonomous vehicles (AVs) in dense traffic is challenging, often leading to overly conservative behavior and unmet planning objectives. This challenge stems fro…
DDAT: Diffusion Policies Enforcing Dynamically Admissible Robot Trajectories
Jean-Baptiste Bouvier, Kanghyun Ryu, Kartik Nagpal +3
Diffusion models excel at creating images and videos thanks to their multimodal generative capabilities. These same capabilities have made diffusion models increasingly popular in…
Risk-Sensitive Orbital Debris Collision Avoidance using Distributionally Robust Chance Constraints
Kanghyun Ryu, Jean-Baptiste Bouvier, Shazaib Lalani +2
The exponential increase in orbital debris and active satellites will lead to congested orbits, necessitating more frequent collision avoidance maneuvers by satellites. To minimize…
CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Models
Kanghyun Ryu, Qiayuan Liao, Zhongyu Li +3
Curriculum learning is a training mechanism in reinforcement learning (RL) that facilitates the achievement of complex policies by progressively increasing the task difficulty duri…
Adaptive Teaching in Heterogeneous Agents: Balancing Surprise in Sparse Reward Scenarios
Emma Clark, Kanghyun Ryu, Negar Mehr
Learning from Demonstration (LfD) can be an efficient way to train systems with analogous agents by enabling ``Student'' agents to learn from the demonstrations of the most experie…
Integrating Predictive Motion Uncertainties with Distributionally Robust Risk-Aware Control for Safe Robot Navigation in Crowds
Kanghyun Ryu, Negar Mehr
Ensuring safe navigation in human-populated environments is crucial for autonomous mobile robots. Although recent advances in machine learning offer promising methods to predict hu…