9 citations · 11 across the 7 of their papers we have counts for
7 papers
Foundation Models for Rapid Autonomy Validation
Alec Farid, Peter Schleede, Aaron Huang +1
We are motivated by the problem of autonomous vehicle performance validation. A key challenge is that an autonomous vehicle requires testing in every kind of driving scenario it co…
Task-Relevant Failure Detection for Trajectory Predictors in Autonomous Vehicles
Alec Farid, Sushant Veer, Boris Ivanovic +2
In modern autonomy stacks, prediction modules are paramount to planning motions in the presence of other mobile agents. However, failures in prediction modules can mislead the down…
Towards a Framework for Comparing the Complexity of Robotic Tasks
Michelle Ho, Alec Farid, Anirudha Majumdar
We are motivated by the problem of comparing the complexity of one robotic task relative to another. To this end, we define a notion of reduction that formalizes the following intu…
Failure Prediction with Statistical Guarantees for Vision-Based Robot Control
Alec Farid, David Snyder, Allen Z. Ren +1
We are motivated by the problem of performing failure prediction for safety-critical robotic systems with high-dimensional sensor observations (e.g., vision). Given access to a bla…
Task-Driven Detection of Distribution Shifts with Statistical Guarantees for Robot Learning
Alec Farid, Sushant Veer, Divyanshu Pachisia +1
Our goal is to perform out-of-distribution (OOD) detection, i.e., to detect when a robot is operating in environments drawn from a different distribution than the ones used to trai…
Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform Stability
Alec Farid, Anirudha Majumdar
We are motivated by the problem of providing strong generalization guarantees in the context of meta-learning. Existing generalization bounds are either challenging to evaluate or…
PAC-Bayes Control: Learning Policies that Provably Generalize to Novel Environments
Anirudha Majumdar, Alec Farid, Anoopkumar Sonar
Our goal is to learn control policies for robots that provably generalize well to novel environments given a dataset of example environments. The key technical idea behind our appr…