Shashank Rao Marpally

Shashank Rao Marpally, Pranav Goyal, Harold Soh

Current social navigation methods and benchmarks primarily focus on proxemics and task efficiency. While these factors are important, qualitative aspects such as perceptions of a robot's social competence are equally crucial for successful adoption and integration into human environments. We propose a more comprehensive evaluation of social navigation through scenario-based testing, where specific human-robot interaction scenarios can reveal key robot behaviors. However, creating such scenarios is often labor-intensive and complex. In this work, we address this challenge by introducing a pipeline that automates the generation of context-, and location-appropriate social navigation scenarios, ready for simulation. Our pipeline transforms simple scenario metadata into detailed textual scenarios, infers pedestrian and robot trajectories, and simulates pedestrian behaviors, which enables more controlled evaluation. We leverage the social reasoning and code-generation capabilities of Large Language Models (LLMs) to streamline scenario generation and translation. Our experiments show that our pipeline produces realistic scenarios and significantly improves scenario translation over naive LLM prompting. Additionally, we present initial feedback from a usability study with social navigation experts and a case-study demonstrating a scenario-based evaluation of three navigation algorithms.

Pulkit Verma, Shashank Rao Marpally, Siddharth Srivastava

Several approaches have been developed for answering users' specific questions about AI behavior and for assessing their core functionality in terms of primitive executable actions. However, the problem of summarizing an AI agent's broad capabilities for a user is comparatively new. This paper presents an algorithm for discovering from scratch the suite of high-level "capabilities" that an AI system with arbitrary internal planning algorithms/policies can perform. It computes conditions describing the applicability and effects of these capabilities in user-interpretable terms. Starting from a set of user-interpretable state properties, an AI agent, and a simulator that the agent can interact with, our algorithm returns a set of high-level capabilities with their parameterized descriptions. Empirical evaluation on several game-based scenarios shows that this approach efficiently learns descriptions of various types of AI agents in deterministic, fully observable settings. User studies show that such descriptions are easier to understand and reason with than the agent's primitive actions.

Mehrdad Zakershahrak, Shashank Rao Marpally, Akshay Sharma et al.

Prior work on generating explanations in a planning and decision-making context has focused on providing the rationale behind an AI agent's decision making. While these methods provide the right explanations from the explainer's perspective, they fail to heed the cognitive requirement of understanding an explanation from the explainee's (the human's) perspective. In this work, we set out to address this issue by first considering the influence of information order in an explanation, or the progressiveness of explanations. Intuitively, progression builds later concepts on previous ones and is known to contribute to better learning. In this work, we aim to investigate similar effects during explanation generation when an explanation is broken into multiple parts that are communicated sequentially. The challenge here lies in modeling the humans' preferences for information order in receiving such explanations to assist understanding. Given this sequential process, a formulation based on goal-based MDP for generating progressive explanations is presented. The reward function of this MDP is learned via inverse reinforcement learning based on explanations that are retrieved via human subject studies. We first evaluated our approach on a scavenger-hunt domain to demonstrate its effectively in capturing the humans' preferences. Upon analyzing the results, it revealed something more fundamental: the preferences arise strongly from both domain dependent and independence features. The correlation with domain independent features pushed us to verify this result further in an escape room domain. Results confirmed our hypothesis that the process of understanding an explanation was a dynamic process. The human preference that reflected this aspect corresponded exactly to the progression for knowledge assimilation hidden deeper in our cognitive process.

Pulkit Verma, Shashank Rao Marpally, Siddharth Srivastava

This paper develops a new approach for estimating an interpretable, relational model of a black-box autonomous agent that can plan and act. Our main contributions are a new paradigm for estimating such models using a minimal query interface with the agent, and a hierarchical querying algorithm that generates an interrogation policy for estimating the agent's internal model in a vocabulary provided by the user. Empirical evaluation of our approach shows that despite the intractable search space of possible agent models, our approach allows correct and scalable estimation of interpretable agent models for a wide class of black-box autonomous agents. Our results also show that this approach can use predicate classifiers to learn interpretable models of planning agents that represent states as images.