1 citations · 1 across the 6 of their papers we have counts for
7 papers
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…
Learning Control Policies to Provably Satisfy Hard Affine Constraints for Black-Box Hybrid Dynamical Systems
Aayushi Shrivastava, Kartik Nagpal, Sairam Jinkala +2
Ensuring safety for black-box hybrid dynamical systems presents significant challenges due to their instantaneous state jumps and unknown explicit nonlinear dynamics. Existing solu…
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…
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…