138 citations · 402 across the 159 of their papers we have counts for
14 papers · 1 filter
Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation
Jiachen Li, David Isele, Kanghoon Lee +3
Deep reinforcement learning (DRL) provides a promising way for intelligent agents (e.g., autonomous vehicles) to learn to navigate complex scenarios. However, DRL with neural netwo…
Large-Scale Multi-Robot Assembly Planning for Autonomous Manufacturing
Kyle Brown, Dylan M. Asmar, Mac Schwager +1
Mobile autonomous robots have the potential to revolutionize manufacturing processes. However, employing large robot fleets in manufacturing requires addressing challenges includin…
Constrained Hierarchical Monte Carlo Belief-State Planning
Arec Jamgochian, Hugo Buurmeijer, Kyle H. Wray +2
Optimal plans in Constrained Partially Observable Markov Decision Processes (CPOMDPs) maximize reward objectives while satisfying hard cost constraints, generalizing safe planning…
Predicting Future Spatiotemporal Occupancy Grids with Semantics for Autonomous Driving
Maneekwan Toyungyernsub, Esen Yel, Jiachen Li +1
For autonomous vehicles to proactively plan safe trajectories and make informed decisions, they must be able to predict the future occupancy states of the local environment. Howeve…
SAVME: Efficient Safety Validation for Autonomous Systems Using Meta-Learning
Marc R. Schlichting, Nina V. Boord, Anthony L. Corso +1
Discovering potential failures of an autonomous system is important prior to deployment. Falsification-based methods are often used to assess the safety of such systems, but the co…
Rank2Tell: A Multimodal Driving Dataset for Joint Importance Ranking and Reasoning
Enna Sachdeva, Nakul Agarwal, Suhas Chundi +5
The widespread adoption of commercial autonomous vehicles (AVs) and advanced driver assistance systems (ADAS) may largely depend on their acceptance by society, for which their per…
A Holistic Assessment of the Reliability of Machine Learning Systems
Anthony Corso, David Karamadian, Romeo Valentin +2
As machine learning (ML) systems increasingly permeate high-stakes settings such as healthcare, transportation, military, and national security, concerns regarding their reliabilit…
Deep Normalizing Flows for State Estimation
Harrison Delecki, Liam A. Kruse, Marc R. Schlichting +1
Safe and reliable state estimation techniques are a critical component of next-generation robotic systems. Agents in such systems must be able to reason about the intentions and tr…
AVOIDDS: Aircraft Vision-based Intruder Detection Dataset and Simulator
Elysia Q. Smyers, Sydney M. Katz, Anthony L. Corso +1
Designing robust machine learning systems remains an open problem, and there is a need for benchmark problems that cover both environmental changes and evaluation on a downstream t…
BetaZero: Belief-State Planning for Long-Horizon POMDPs using Learned Approximations
Robert J. Moss, Anthony Corso, Jef Caers +1
Real-world planning problems, including autonomous driving and sustainable energy applications like carbon storage and resource exploration, have recently been modeled as partially…
Experience Filter: Using Past Experiences on Unseen Tasks or Environments
Anil Yildiz, Esen Yel, Anthony L. Corso +3
One of the bottlenecks of training autonomous vehicle (AV) agents is the variability of training environments. Since learning optimal policies for unseen environments is often very…
Model-based Validation as Probabilistic Inference
Harrison Delecki, Anthony Corso, Mykel J. Kochenderfer
Estimating the distribution over failures is a key step in validating autonomous systems. Existing approaches focus on finding failures for a small range of initial conditions or m…
Optimizing Carbon Storage Operations for Long-Term Safety
Yizheng Wang, Markus Zechner, Gege Wen +4
To combat global warming and mitigate the risks associated with climate change, carbon capture and storage (CCS) has emerged as a crucial technology. However, safely sequestering C…
Incorporating Human Path Preferences in Robot Navigation with Minimal Interventions
Oriana Peltzer, Dylan M. Asmar, Mac Schwager +1
Robots that can effectively understand human intentions from actions are crucial for successful human-robot collaboration. In this work, we address the challenge of a robot navigat…