10 papers
Generalists Act, Specialists Intervene: Modular Stage-Selective Reinforcement Learning for Vision-Language-Action Manipulation
Chongyu Zhu, Jaden Hinds, Hyegang Kim +3
Vision-language-action (VLA) models often struggle in the precision-critical phases of multi-stage manipulation tasks. To mitigate this issue, VLA models can be used in conjunction…
WireCraft: A Simulation Benchmark for Industrial DLO Manipulation
Chongyu Zhu, Ramy ElMallah, Hyegang Kim +5
Deformable Linear Objects (DLOs), such as wires and cables, are central to industrial assembly. Unlike rigid objects, whose state is captured by a 6-DoF pose, DLOs have an infinite…
Offline Discovery of Interpretable Skills from Multi-Task Trajectories
Chongyu Zhu, Mithun Vanniasinghe, Jiayu Chen +1
Hierarchical Imitation Learning is a powerful paradigm for acquiring complex robot behaviors from demonstrations. A central challenge, however, lies in discovering reusable skills…
Improving Generative Behavior Cloning via Self-Guidance and Adaptive Chunking
Junhyuk So, Chiwoong Lee, Shinyoung Lee +2
Generative Behavior Cloning (GBC) is a simple yet effective framework for robot learning, particularly in multi-task settings. Recent GBC methods often employ diffusion policies wi…
Ergodic Risk Measures: Towards a Risk-Aware Foundation for Continual Reinforcement Learning
Juan Sebastian Rojas, Chi-Guhn Lee
Continual reinforcement learning (continual RL) seeks to formalize the notions of lifelong learning and endless adaptation in RL. In particular, the aim of continual RL is to devel…
Score the Steps, Not Just the Goal: VLM-Based Subgoal Evaluation for Robotic Manipulation
Ramy ElMallah, Krish Chhajer, Chi-Guhn Lee
Robot learning papers typically report a single binary success rate (SR), which obscures where a policy succeeds or fails along a multi-step manipulation task. We argue that subgoa…
A Contrastive Diffusion-based Network (CDNet) for Time Series Classification
Yaoyu Zhang, Chi-Guhn Lee
Deep learning models are widely used for time series classification (TSC) due to their scalability and efficiency. However, their performance degrades under challenging data condit…
Learning Bilateral Team Formation in Cooperative Multi-Agent Reinforcement Learning
Koorosh Moslemi, Chi-Guhn Lee
Team formation and the dynamics of team-based learning have drawn significant interest in the context of Multi-Agent Reinforcement Learning (MARL). However, existing studies primar…
A Differential Perspective on Distributional Reinforcement Learning
Juan Sebastian Rojas, Chi-Guhn Lee
To date, distributional reinforcement learning (distributional RL) methods have exclusively focused on the discounted setting, where an agent aims to optimize a discounted sum of r…
Burning RED: Unlocking Subtask-Driven Reinforcement Learning and Risk-Awareness in Average-Reward Markov Decision Processes
Juan Sebastian Rojas, Chi-Guhn Lee
Average-reward Markov decision processes (MDPs) provide a foundational framework for sequential decision-making under uncertainty. However, average-reward MDPs have remained largel…