Rearrangement: A Challenge for Embodied AI (2011.01975v1)

Abstract: We describe a framework for research and evaluation in Embodied AI. Our proposal is based on a canonical task: Rearrangement. A standard task can focus the development of new techniques and serve as a source of trained models that can be transferred to other settings. In the rearrangement task, the goal is to bring a given physical environment into a specified state. The goal state can be specified by object poses, by images, by a description in language, or by letting the agent experience the environment in the goal state. We characterize rearrangement scenarios along different axes and describe metrics for benchmarking rearrangement performance. To facilitate research and exploration, we present experimental testbeds of rearrangement scenarios in four different simulation environments. We anticipate that other datasets will be released and new simulation platforms will be built to support training of rearrangement agents and their deployment on physical systems.

• The paper introduces a standardized rearrangement task that leverages POMDPs and diverse simulation environments to benchmark embodied AI capabilities.
• The paper presents task completion, path efficiency, and resource utilization as core metrics for evaluating intelligent system performance.
• The paper discusses realistic embodiment with integrated vision and sensing, paving the way for future research in dynamic, unstructured settings.

Overview of the Paper on Rearrangement as a Challenge for Embodied AI

The paper, "Rearrangement: A Challenge for Embodied AI," proposes a structured task designed to advance the research and evaluation of Embodied AI through a focus on rearrangement tasks. This canonical task aims to provide a standardized benchmark for assessing the capabilities of intelligent systems in actively interacting with and modifying environments to achieve specific goal states. The rearrangement task requires an agent to transition an environment from a given configuration to a desired state, specified through various means such as object poses, task descriptions in language, or visual examples of the target configuration.

Core Contributions and Methodologies

1. Task Specification and Framework: The paper meticulously defines the rearrangement task using the language of Partially Observable Markov Decision Processes (POMDPs). This approach encapsulates the complexity of real-world environments and allows for a flexible goal specification. Importantly, the task framework is structured to cover a range of complexities, from navigation and object manipulation to cognitive planning and decision-making.
2. Evaluation Metrics: The authors propose "task completion" as the primary evaluation metric, which quantifies the success of an agent by the percentage of goals it achieves. Besides this, the paper recommends additional metrics such as path efficiency and computational resource utilization, which are crucial for real-world implementations. The comprehensive evaluation protocols are designed to emphasize the trade-offs between task success and efficiency, fostering development towards practical systems.
3. Simulation Environments and Benchmarks: To promote immediate research, the paper introduces a set of experimental testbeds spanning simulation environments such as AI2-THOR, Habitat, RLBench, and SAPIEN. These environments support various scenarios from tabletop object organization to full-house rearrangement, encompassing diverse interaction challenges through different levels of abstraction and manipulation complexity.
4. Embodiment and Sensory Dynamics: The discussion extends into the spectrum of agent embodiments, ranging from abstracted interaction mechanisms like "magic pointers" to fully simulated robots with articulated arms. The paper advocates for realistic onboard sensing, integrating vision, depth, and possibly haptic sensations to mimic real-world sensory conditions and propel meaningful research development.

Implications and Future Extensions

The implications of this task are extensive. By establishing a well-defined and broadly applicable task, the authors catalyze progress in developing general Embodied AI systems that can intuitively perceive and manipulate their environments. The focus on end-to-end evaluation aligns with real-world constraints and drives the development of robust systems capable of real-time processing and decision-making.

Furthermore, the paper lays the groundwork for future research directions, which could include the manipulation of deformable objects, transformation of object states, multi-agent rearrangement scenarios, and interactive learning with humans. Through these extensions, the paper emphasizes the potential for Embodied AI systems to handle increasingly complex and nuanced tasks within dynamic and unstructured environments.

As an overarching contribution, this work bridges the gap between theoretical AI models and tangible applications, pushing the envelope on what intelligent systems can achieve in physical and simulated environments. The comprehensive formulation of the rearrangement task paves the way for a new era of research in Embodied AI, aiming to produce systems that seamlessly integrate into human-centric settings and perform sophisticated tasks with precision and adaptability.