BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments (2108.03332v1)

Abstract: We introduce BEHAVIOR, a benchmark for embodied AI with 100 activities in simulation, spanning a range of everyday household chores such as cleaning, maintenance, and food preparation. These activities are designed to be realistic, diverse, and complex, aiming to reproduce the challenges that agents must face in the real world. Building such a benchmark poses three fundamental difficulties for each activity: definition (it can differ by time, place, or person), instantiation in a simulator, and evaluation. BEHAVIOR addresses these with three innovations. First, we propose an object-centric, predicate logic-based description language for expressing an activity's initial and goal conditions, enabling generation of diverse instances for any activity. Second, we identify the simulator-agnostic features required by an underlying environment to support BEHAVIOR, and demonstrate its realization in one such simulator. Third, we introduce a set of metrics to measure task progress and efficiency, absolute and relative to human demonstrators. We include 500 human demonstrations in virtual reality (VR) to serve as the human ground truth. Our experiments demonstrate that even state of the art embodied AI solutions struggle with the level of realism, diversity, and complexity imposed by the activities in our benchmark. We make BEHAVIOR publicly available at behavior.stanford.edu to facilitate and calibrate the development of new embodied AI solutions.

An Analysis of the BEHAVIOR Benchmark for Embodied AI

The paper "BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments" presents a comprehensive benchmarking tool designed for evaluating embodied AI agents using realistic simulations of household activities. The benchmark is developed by a team from Stanford University, and it aims to bridge the gap between human-centric everyday tasks and the capabilities of contemporary AI systems within virtual environments.

BEHAVIOR comprises 100 distinctive household activities encoded in virtual environments, encapsulating tasks such as cleaning, maintenance, and food preparation. The significance of this benchmark is underscored by the increasing interest in embodied AI, which deals with agents capable of interacting with the physical world through perception, reasoning, and manipulation. The benchmark seeks to foster the growth of AI capable of handling the intricacies and diversities of human everyday life chores.

Key Innovations

The paper outlines several innovative approaches in the creation of BEHAVIOR:

1. Object-Centric Predicate Logic for Activity Representation: The development of the BEHAVIOR Domain Definition Language (BDDL) enables the structured description of activities' initial and goal states using predicate logic. This allows for the formulation of diverse and complex tasks, facilitating the generation of numerous activity instances.
2. Simulator-Agnostic Features: The authors identify key requirements for the simulation environment, ensuring that BEHAVIOR can be instantiated across various platforms while demonstrating its implementation in iGibson 2.0.
3. Evaluation Metrics: BEHAVIOR includes a comprehensive evaluation framework with metrics that assess task progress and efficiency. These metrics provide granular insights into agent performance relative to human benchmarks, thanks to a dataset of 500 human demonstrations in VR.

Challenges in Defining Activities

The authors recognize several challenges unique to benchmarking embodied AI:

• Activity Definition: Activities vary based on context, time, and the entities involved, thus necessitating a flexible yet standardized method of definition.
• Realization and Simulation: Translating the logical specifications of activities into realistic and feasible simulation setups requires meticulous engineering.
• Objective Evaluation: Measuring success in complex tasks involves multi-dimensional metrics that account for both efficiency and effectiveness in task execution.

Implications and Observations

Through rigorous testing with state-of-the-art AI systems, the BEHAVIOR benchmark exposes significant limitations in current AI capabilities. The results demonstrate that contemporary reinforcements learning algorithms, like SAC and PPO, struggle with the benchmark's demands for long-horizon planning and task complexity.

The implications of BEHAVIOR are substantial. By challenging AI with tasks approximating real-world complexity and variability, the benchmark encourages the development of more robust AI solutions. It sets a new standard for evaluating embodied AI, emphasizing the importance of ecological fidelity and diversity, and pushes research towards overcoming the sim-to-real gap.

Future Directions

The paper suggests that BEHAVIOR could catalyze advancements in hierarchical reinforcement learning and task-and-motion-planning solutions that can tackle the intricacies of human-like tasks. Furthermore, the open-source nature of BEHAVIOR positions it as a unifying tool for the embodied AI community, guiding efforts in developing AI systems that can assist with real-life household activities competently.

In conclusion, the BEHAVIOR benchmark represents a significant leap forward in the quest to create highly capable embodied AI. By providing a thorough and realistic testbed for AI agents, BEHAVIOR not only sets a high bar for current AI research but also outlines the pathway forward for the development of AI that can seamlessly integrate into the daily human environment.