LaNMP: A Language-Conditioned Mobile Manipulation Benchmark for Autonomous Robots

As robots that follow natural language become more capable and prevalent, we need a benchmark to holistically develop and evaluate their ability to solve long-horizon mobile manipulation tasks in large, diverse environments. To tackle this challenge, robots must use visual and language understanding, navigation, and manipulation capabilities. Existing datasets do not integrate all these aspects, restricting their efficacy as benchmarks. To address this gap, we present the Language, Navigation, Manipulation, Perception (LaNMP, pronounced Lamp) dataset and demonstrate the benefits of integrating these four capabilities and various modalities. LaNMP comprises 574 trajectories across eight simulated and real-world environments for long-horizon room-to-room pick-and-place tasks specified by natural language. Every trajectory consists of over 20 attributes, including RGB-D images, segmentations, and the poses of the robot body, end-effector, and grasped objects. We fine-tuned and tested two models in simulation, and evaluated a third on a physical robot, to demonstrate the benchmark's applicability in development and evaluation, as well as making models more sample efficient. The models performed suboptimally compared to humans; however, showed promise in increasing model sample efficiency, indicating significant room for developing more sample efficient multimodal mobile manipulation models using our benchmark.

Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse

Machine learning models are often used to automate or support decisions in applications such as lending and hiring. In such settings, consumer protection rules mandate that we provide a list of "principal reasons" to consumers who receive adverse decisions. In practice, lenders and employers identify principal reasons by returning the top-scoring features from a feature attribution method. In this work, we study how such practices align with one of the underlying goals of consumer protection - recourse - i.e., educating individuals on how they can attain a desired outcome. We show that standard attribution methods can mislead individuals by highlighting reasons without recourse - i.e., by presenting consumers with features that cannot be changed to achieve recourse. We propose to address these issues by scoring features on the basis of responsiveness - i.e., the probability that an individual can attain a desired outcome by changing a specific feature. We develop efficient methods to compute responsiveness scores for any model and any dataset under complex actionability constraints. We present an extensive empirical study on the responsiveness of explanations in lending and demonstrate how responsiveness scores can be used to construct feature-highlighting explanations that lead to recourse and mitigate harm by flagging instances with fixed predictions.