12 citations · 45 across the 7 of their papers we have counts for
7 papers
M-GRPO: Stabilizing Self-Supervised Reinforcement Learning for Large Language Models with Momentum-Anchored Policy Optimization
Bizhe Bai, Hongming Wu, Peng Ye +1
Self-supervised reinforcement learning (RL) presents a promising approach for enhancing the reasoning capabilities of Large Language Models (LLMs) without reliance on expensive hum…
Hyperparameter Auto-tuning in Self-Supervised Robotic Learning
Jiancong Huang, Juan Rojas, Matthieu Zimmer +3
Policy optimization in reinforcement learning requires the selection of numerous hyperparameters across different environments. Fixing them incorrectly may negatively impact optimi…
Endowing Robots with Longer-term Autonomy by Recovering from External Disturbances in Manipulation through Grounded Anomaly Classification and Recovery Policies
Hongmin Wu, Shuangqi Luo, Longxin Chen +5
Robot manipulation is increasingly poised to interact with humans in co-shared workspaces. Despite increasingly robust manipulation and control algorithms, failure modes continue t…
Fast, Robust, and Versatile Event Detection through HMM Belief State Gradient Measures
Shuangqi Luo, Hongmin Wu, Hongbin Lin +3
Event detection is a critical feature in data-driven systems as it assists with the identification of nominal and anomalous behavior. Event detection is increasingly relevant in ro…
Learning Human-Robot Collaboration Insights through the Integration of Muscle Activity in Interaction Motion Models
Longxin Chen, Juan Rojas, Shuangda Duan +1
Recent progress in human-robot collaboration makes fast and fluid interactions possible, even when human observations are partial and occluded. Methods like Interaction Probabilist…
Recovering from External Disturbances in Online Manipulation through State-Dependent Revertive Recovery Policies
Hongmin Wu, Hongbin Lin, Shuangqi Luo +3
Robots are increasingly entering uncertain and unstructured environments. Within these, robots are bound to face unexpected external disturbances like accidental human or tool coll…
Robot Introspection with Bayesian Nonparametric Vector Autoregressive Hidden Markov Models
Hongmin Wu, Hongbin Lin, Yisheng Guan +2
Robot introspection, as opposed to anomaly detection typical in process monitoring, helps a robot understand what it is doing at all times. A robot should be able to identify its a…