1 citations · 1 across the 6 of their papers we have counts for
6 papers
Fast Confidence-Aware Human Prediction via Hardware-accelerated Bayesian Inference for Safe Robot Navigation
Michael Lu, Minh Bui, Xubo Lyu +1
As robots increasingly integrate into everyday environments, ensuring their safe navigation around humans becomes imperative. Efficient and safe motion planning requires robots to…
Learning Robust Policies via Interpretable Hamilton-Jacobi Reachability-Guided Disturbances
Hanyang Hu, Xilun Zhang, Xubo Lyu +1
Deep Reinforcement Learning (RL) has shown remarkable success in robotics with complex and heterogeneous dynamics. However, its vulnerability to unknown disturbances and adversaria…
Task-Oriented Koopman-Based Control with Contrastive Encoder
Xubo Lyu, Hanyang Hu, Seth Siriya +2
We present task-oriented Koopman-based control that utilizes end-to-end reinforcement learning and contrastive encoder to simultaneously learn the Koopman latent embedding, operato…
Asynchronous, Option-Based Multi-Agent Policy Gradient: A Conditional Reasoning Approach
Xubo Lyu, Amin Banitalebi-Dehkordi, Mo Chen +1
Cooperative multi-agent problems often require coordination between agents, which can be achieved through a centralized policy that considers the global state. Multi-agent policy g…
Optimal Control-Based Baseline for Guided Exploration in Policy Gradient Methods
Xubo Lyu, Site Li, Seth Siriya +2
In this paper, a novel optimal control-based baseline function is presented for the policy gradient method in deep reinforcement learning (RL). The baseline is obtained by computin…
TTR-Based Reward for Reinforcement Learning with Implicit Model Priors
Xubo Lyu, Mo Chen
Model-free reinforcement learning (RL) is a powerful approach for learning control policies directly from high-dimensional state and observation. However, it tends to be data-ineff…