2nd Workshop on Out-of-Distribution Generalization in Robotics
Towards Reliable Learning-Based Autonomy

Workshop Overview

As we increasingly rely on AI-driven robotic autonomy stacks to contend with new tasks and unstructured environments, prudence requires that we also acknowledge the limitations of these systems. In practice, robots often fail to meet our expectations when deployed in the real world, where distribution shifts from training data, conditions evolve, and rare unpredictable corner cases degrade the reliability of robot systems powered by ML models. While these reliability concerns are well known, we lack a comprehensive roadmap to address these issues at all levels of a learned autonomy stack. Therefore, this workshop aims to bring together a diverse group of researchers and industry practitioners to chart a roadmap for 1) addressing the disruptive impact of distributional shifts and out-of-distribution (OOD) scenarios on robot performance and 2) examining opportunities to enable trustworthy autonomy in unfamiliar, OOD domains by leveraging emerging tools and novel insights.

Since the inaugural offering of this workshop at CoRL 2023, we have witnessed the rapid emergence of foundational tools, particularly multi-modal large language models, that provide a promising pathway to build systems that are trustworthy beyond an individual robot’s limited training distribution. Hence, we aim to rekindle a timely discussion on OOD reliability and trustworthy autonomy: Are we now in a position to unblock ourselves and build highly reliable and trustworthy robotic systems for the real world? What key challenges remain unsolved? Our diverse set of speakers and panelists reflects the opinions and operational demands from experts in core ML, to autonomous vehicles, household and warehouse manipulation, and drone racing, thereby providing a unifying view of the OOD problem across application domains.

This workshop aims to bring together a diverse group of researchers and industry practitioners to chart a roadmap for 1) addressing the disruptive impact of distributional shifts and out-of-distribution (OOD) scenarios on robot performance and 2) examining opportunities to enable trustworthy autonomy in unfamiliar, OOD domains leveraging new tools, such as foundation models. Therefore, this workshop broadly aims to address gaps between academia and practice by igniting discussions on research challenges and their synergies at all timescales crucial to improving reliability and deploying robust autonomous systems:

1. Safeguarding against OOD scenarios: How can we detect, predict, or reason about OOD scenarios that an AI-based robot is encountering to inform safe decision making? For example, can we leverage full-stack sensory information to anticipate errors and enact interventions to mitigate the consequences? What new tools and methods will help increase the trustworthiness of robots operating beyond the training distribution? 

2. Extrapolation beyond nominal conditions: How can we develop, maintain, and utilize contextual understanding of a robot’s task and environment to facilitate generalization? What role should internet-scale models, e.g., LLMs and VLMs, play in extrapolating to OOD scenarios and beyond a robot’s training data? 

3. Continual data lifecycle as we deploy, evaluate, and retrain learning-enabled robots: How can we efficiently collect data throughout deployment and develop learning algorithms that further improve system robustness and performance? What are the appropriate procedures for re-evaluating and re-certifying robots?

4. Towards task-relevant definitions of domain shift: There are many ways to define what makes data OOD, with specific choices depending on problem formulations and application contexts. What are the most task-relevant definitions for common robotics problems? How can we quantify generalization, and how will they influence approaches and experimental evaluations?

Invited Speakers and Panelists

Organizers

Sponsors