8 citations · 26 across the 22 of their papers we have counts for
20 papers · 1 filter
PrefMoE: Robust Preference Modeling with Mixture-of-Experts Reward Learning
Ziqin Yuan, Ruiqi Wang, Dezhong Zhao +2
Preference-based reinforcement learning offers a scalable alternative to manual reward engineering by learning reward structures from comparative feedback. However, large-scale pre…
Pre-Execution Safety Gate & Task Safety Contracts for LLM-Controlled Robot Systems
Ike Obi, Vishnunandan L. N. Venkatesh, Weizheng Wang +5
Large Language Models (LLMs) are increasingly used to convert task commands into robot-executable code, however this pipeline lacks validation gates to detect unsafe and defective…
Move-Then-Operate: Behavioral Phasing for Human-Like Robotic Manipulation
Haoming Xu, Lei Lei, Jie Gu +3
We present Move-Then-Operate, a Vision language action framework that explicitly decouples robotic manipulation into two distinct behavioral phases: coarse relocation (move) and co…
PRIMT: Preference-based Reinforcement Learning with Multimodal Feedback and Trajectory Synthesis from Foundation Models
Ruiqi Wang, Dezhong Zhao, Ziqin Yuan +5
Preference-based reinforcement learning (PbRL) has emerged as a promising paradigm for teaching robots complex behaviors without reward engineering. However, its effectiveness is o…
AutoBio: A Simulation and Benchmark for Robotic Automation in Digital Biology Laboratory
Zhiqian Lan, Yuxuan Jiang, Ruiqi Wang +12
Vision-language-action (VLA) models have shown promise as generalist robotic policies by jointly leveraging visual, linguistic, and proprioceptive modalities to generate action tra…
SafePlan: Leveraging Formal Logic and Chain-of-Thought Reasoning for Enhanced Safety in LLM-based Robotic Task Planning
Ike Obi, Vishnunandan L. N. Venkatesh, Weizheng Wang +5
Robotics researchers increasingly leverage large language models (LLM) in robotics systems, using them as interfaces to receive task commands, generate task plans, form team coalit…