Post-hoc explanation methods are gaining popularity for interpreting, understanding, and debugging neural networks. Most analyses using such methods explain decisions in response to inputs drawn from the test set. However, the test set may have few examples that trigger some model behaviors, such as high-confidence failures or ambiguous classifications. To address these challenges, we introduce a flexible model inspection framework: Bayes-TrEx. Given a data distribution, Bayes-TrEx finds in-distribution examples which trigger a specified prediction confidence. We demonstrate several use cases of Bayes-TrEx, including revealing highly confident (mis)classifications, visualizing class boundaries via ambiguous examples, understanding novel-class extrapolation behavior, and exposing neural network overconfidence. We use Bayes-TrEx to study classifiers trained on CLEVR, MNIST, and Fashion-MNIST, and we show that this framework enables more flexible holistic model analysis than just inspecting the test set. Code and supplemental material are available at https://github.com/serenabooth/Bayes-TrEx.

Temporal logics are useful for providing concise descriptions of system behavior, and have been successfully used as a language for goal definitions in task planning. Prior works on inferring temporal logic specifications have focused on "summarizing" the input dataset - i.e., finding specifications that are satisfied by all plan traces belonging to the given set. In this paper, we examine the problem of inferring specifications that describe temporal differences between two sets of plan traces. We formalize the concept of providing such contrastive explanations, then present BayesLTL - a Bayesian probabilistic model for inferring contrastive explanations as linear temporal logic (LTL) specifications. We demonstrate the robustness and scalability of our model for inferring accurate specifications from noisy data and across various benchmark planning domains.

When observing task demonstrations, human apprentices are able to identify whether a given task is executed correctly long before they gain expertise in actually performing that task. Prior research into learning from demonstrations (LfD) has failed to capture this notion of the acceptability of an execution; meanwhile, temporal logics provide a flexible language for expressing task specifications. Inspired by this, we present Bayesian specification inference, a probabilistic model for inferring task specification as a temporal logic formula. We incorporate methods from probabilistic programming to define our priors, along with a domain-independent likelihood function to enable sampling-based inference. We demonstrate the efficacy of our model for inferring true specifications with over 90% similarity between the inferred specification and the ground truth, both within a synthetic domain and a real-world table setting task.

Manipulation of deformable linear objects (DLOs) has potential applications in the fields of aerospace and automotive assembly. In this paper, we introduce a problem formulation for attaching a set of interlinked DLOs to a support structure using a set of clamping points. The formulation describes the manipulation planning problem in terms of known clamp poses; predetermined ideal clamping locations on the cables, called “reference points;” and a set of finite gripping points on the DLOs. We also present a prototype algorithm that generates a solution in terms of primitive manipulation actions. The algorithm guarantees that no interlink constraints are violated at any stage of manipulation. We incorporate gravity in the computation of a DLO shape and propose a property linking geometrically similar cable shapes across the space of cable length and stiffness. This property allows for the computation of solutions for unit length and scaling of these solutions to appropriate length, potentially resulting in faster shape computation.

Manipulation of deformable linear objects (DLO) has potential applications in aerospace and automotive assembly. The current literature on planning for deformable objects focuses on a single DLO at a time. In this paper, we provide a problem formulation for attaching a set of interlinked DLOs to a support structure through a set of clamping points. We also present a prototype algorithm that generates a solution in terms of primitive manipulation actions. The algorithm guarantees that none of the interlink constraints are violated. Finally, we incorporate gravity in the computation of a DLO shape and propose a property linking geometrically similar cable shapes across the space of cable length and stiffness. This property allows for computation of solutions for unit length and scaling of the solutions to appropriate length, thus potentially making shape computations faster.