Search Results for author:
Found 9 papers, 2 papers with code
LADEV: A Language-Driven Testing and Evaluation Platform for Vision-Language-Action Models in Robotic Manipulation
This highlights the need for a reliable testing and evaluation platform.
MORTAR: A Model-based Runtime Action Repair Framework for AI-enabled Cyber-Physical Systems
If an unsafe action is detected, MORTAR then initiates a repair process to correct it.
Multilingual Blending: LLM Safety Alignment Evaluation with Language Mixture
As safety remains a crucial concern throughout the development lifecycle of Large Language Models (LLMs), researchers and industrial practitioners have increasingly focused on safeguarding and aligning LLM behaviors with human preferences and ethical standards.
GenSafe: A Generalizable Safety Enhancer for Safe Reinforcement Learning Algorithms Based on Reduced Order Markov Decision Process Model
To address this issue, we introduce in this work a novel Generalizable Safety enhancer (GenSafe) that is able to overcome the challenge of data insufficiency and enhance the performance of SRL approaches.
Online Safety Analysis for LLMs: a Benchmark, an Assessment, and a Path Forward
To bridge this gap, we conduct in this work a comprehensive evaluation of the effectiveness of existing online safety analysis methods on LLMs.
Self-Refined Large Language Model as Automated Reward Function Designer for Deep Reinforcement Learning in Robotics
Then, the performance of the reward function is assessed, and the results are presented back to the LLM for guiding its self-refinement process.
ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning
Motivated by the substantial achievements observed in Large Language Models (LLMs) in the field of natural language processing, recent research has commenced investigations into the application of LLMs for complex, long-horizon sequential task planning challenges in robotics.
Data Generation Method for Learning a Low-dimensional Safe Region in Safe Reinforcement Learning
Safe reinforcement learning aims to learn a control policy while ensuring that neither the system nor the environment gets damaged during the learning process.
Off-Policy Risk-Sensitive Reinforcement Learning Based Constrained Robust Optimal Control
By introducing pseudo controls and risk-sensitive input and state penalty terms, the constrained robust stabilization problem of the original system is converted into an equivalent optimal control problem of an auxiliary system.
