12 papers
In-Context Planning with Latent Temporal Abstractions
Baiting Luo, Yunuo Zhang, Nathaniel S. Keplinger +3
Planning-based reinforcement learning for continuous control is bottlenecked by two practical issues: planning at primitive time scales leads to prohibitive branching and long hori…
ESCORT: Efficient Stein-variational and Sliced Consistency-Optimized Temporal Belief Representation for POMDPs
Yunuo Zhang, Baiting Luo, Ayan Mukhopadhyay +2
In Partially Observable Markov Decision Processes (POMDPs), maintaining and updating belief distributions over possible underlying states provides a principled way to summarize act…
Observation Adaptation via Annealed Importance Resampling for Partially Observable Markov Decision Processes
Yunuo Zhang, Baiting Luo, Ayan Mukhopadhyay +1
Partially observable Markov decision processes (POMDPs) are a general mathematical model for sequential decision-making in stochastic environments under state uncertainty. POMDPs a…
Scalable Decision-Making in Stochastic Environments through Learned Temporal Abstraction
Baiting Luo, Ava Pettet, Aron Laszka +2
Sequential decision-making in high-dimensional continuous action spaces, particularly in stochastic environments, faces significant computational challenges. We explore this challe…
NS-Gym: Open-Source Simulation Environments and Benchmarks for Non-Stationary Markov Decision Processes
Nathaniel S. Keplinger, Baiting Luo, Iliyas Bektas +5
In many real-world applications, agents must make sequential decisions in environments where conditions are subject to change due to various exogenous factors. These non-stationary…
Shrinking POMCP: A Framework for Real-Time UAV Search and Rescue
Yunuo Zhang, Baiting Luo, Ayan Mukhopadhyay +8
Efficient path optimization for drones in search and rescue operations faces challenges, including limited visibility, time constraints, and complex information gathering in urban…
Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes
Baiting Luo, Yunuo Zhang, Abhishek Dubey +1
A fundamental (and largely open) challenge in sequential decision-making is dealing with non-stationary environments, where exogenous environmental conditions change over time. Suc…
Decision Making in Non-Stationary Environments with Policy-Augmented Search
Ava Pettet, Yunuo Zhang, Baiting Luo +5
Sequential decision-making under uncertainty is present in many important problems. Two popular approaches for tackling such problems are reinforcement learning and online search (…
Dynamic Simplex: Balancing Safety and Performance in Autonomous Cyber Physical Systems
Baiting Luo, Shreyas Ramakrishna, Ava Pettet +3
Learning Enabled Components (LEC) have greatly assisted cyber-physical systems in achieving higher levels of autonomy. However, LEC's susceptibility to dynamic and uncertain operat…
ANTI-CARLA: An Adversarial Testing Framework for Autonomous Vehicles in CARLA
Shreyas Ramakrishna, Baiting Luo, Christopher Kuhn +2
Despite recent advances in autonomous driving systems, accidents such as the fatal Uber crash in 2018 show these systems are still susceptible to edge cases. Such systems must be t…
Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems
Shreyas Ramakrishna, Baiting Luo, Yogesh Barve +2
Autonomous Cyber-Physical Systems must often operate under uncertainties like sensor degradation and shifts in the operating conditions, which increases its operational risk. Dynam…
Securing Connected Vehicle Applications with an Efficient Dual Cyber-Physical Blockchain Framework
Xiangguo Liu, Baiting Luo, Ahmed Abdo +2
While connected vehicle (CV) applications have the potential to revolutionize traditional transportation system, cyber and physical attacks on them could be devastating. In this wo…