159 citations · 798 across the 116 of their papers we have counts for
11 papers · 1 filter
Synthesizing Queries via Interactive Sketching
Osbert Bastani, Xin Zhang, Armando Solar-Lezama
We propose a novel approach to program synthesis, focusing on synthesizing database queries. At a high level, our proposed algorithm takes as input a sketch with soft constraints e…
"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations
Himabindu Lakkaraju, Osbert Bastani
As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniq…
MAMPS: Safe Multi-Agent Reinforcement Learning via Model Predictive Shielding
Wenbo Zhang, Osbert Bastani, Vijay Kumar
Reinforcement learning is a promising approach to learning control policies for performing complex multi-agent robotics tasks. However, a policy learned in simulation often fails t…
Robust Model Predictive Shielding for Safe Reinforcement Learning with Stochastic Dynamics
Shuo Li, Osbert Bastani
This paper proposes a framework for safe reinforcement learning that can handle stochastic nonlinear dynamical systems. We focus on the setting where the nominal dynamics are known…
Learning Safe Unlabeled Multi-Robot Planning with Motion Constraints
Arbaaz Khan, Chi Zhang, Shuo Li +6
In this paper, we present a learning approach to goal assignment and trajectory planning for unlabeled robots operating in 2D, obstacle-filled workspaces. More specifically, we tac…
Safe Reinforcement Learning with Nonlinear Dynamics via Model Predictive Shielding
Osbert Bastani
Reinforcement learning is a promising approach to synthesizing policies for challenging robotics tasks. A key problem is how to ensure safety of the learned policy---e.g., that a w…