ARNOLD: A Benchmark for Language-Grounded Task Learning With Continuous States in Realistic 3D Scenes

Abstract: Understanding the continuous states of objects is essential for task learning and planning in the real world. However, most existing task learning benchmarks assume discrete (e.g., binary) object goal states, which poses challenges for the learning of complex tasks and transferring learned policy from simulated environments to the real world. Furthermore, state discretization limits a robot's ability to follow human instructions based on the grounding of actions and states. To tackle these challenges, we present ARNOLD, a benchmark that evaluates language-grounded task learning with continuous states in realistic 3D scenes. ARNOLD is comprised of 8 language-conditioned tasks that involve understanding object states and learning policies for continuous goals. To promote language-instructed learning, we provide expert demonstrations with template-generated language descriptions. We assess task performance by utilizing the latest language-conditioned policy learning models. Our results indicate that current models for language-conditioned manipulations continue to experience significant challenges in novel goal-state generalizations, scene generalizations, and object generalizations. These findings highlight the need to develop new algorithms that address this gap and underscore the potential for further research in this area. Project website: https://arnold-benchmark.github.io.

• The paper introduces ARNOLD, a benchmark offering a photorealistic 3D simulation environment with continuous state representation for nuanced task learning.
• It leverages NVIDIA’s Isaac Sim and over 10,000 curated demonstrations to evaluate diverse language-guided robotic tasks like object manipulation and reorientation.
• Results reveal that existing models struggle to accurately align language instructions with fine-grained physical states, highlighting the need for more integrated algorithmic approaches.

Overview of ARNOLD: A Benchmark for Language-Grounded Task Learning with Continuous States in Realistic 3D Scenes

The paper introduces ARNOLD, a comprehensive benchmark designed for the evaluation and advancement of language-grounded task learning within realistic 3D environments. This benchmark addresses crucial challenges by considering continuous states in task learning, contrasting previous benchmarks which are predominantly based on discrete state representations.

Key Contributions

ARNOLD's contributions are multifaceted, addressing several longstanding challenges in task learning and robot environment interaction:

1. Realistic 3D Interactive Environment: ARNOLD leverages NVIDIA's Isaac Sim platform, providing a photorealistic and physically-accurate simulation environment. This aspect is pivotal for closing the Sim2Real gap, where simulations often diverge from real-world physics and visual fidelity.
2. Continuous State Representation: Unlike prior works which assume discrete object states, ARNOLD models realistic tasks requiring manipulation of continuous states. This is particularly critical for tasks conveying nuanced human instructions, such as adjusting the position or orientation of objects to a specified degree or percentage.
3. Diverse Tasks and Evaluation Splits: ARNOLD incorporates eight tasks, such as PickupObject, ReorientObject, and various drawer/cabinet manipulation tasks, each designed to test different facets of language grounding and continuous state manipulation. The tasks are accompanied by evaluation splits focusing on generalization across novel objects, scenes, and goal states.
4. Data Collection and Generalization: The benchmark includes a substantial dataset, curated and augmented with over 10,000 demonstrations. It incorporates systematic evaluations for model robustness, particularly in novel scenarios, encouraging the development of models adept at generalizing across unseen conditions.

Experimental Setup and Methodologies

The paper extensively evaluates current state-of-the-art models like 6D-CLIPort and PerAct within the ARNOLD benchmark. These evaluations reveal significant limitations in present approaches to handling realistic and fine-grained task learning. Specifically, models struggle with the precise alignment of language instructions with corresponding physical states, highlighting the need for more sophisticated state modeling and generalization techniques.

The benchmark's design also emphasizes learning efficiency, maintaining a clear separation between phases of task execution, thus assisting models in focusing on critical transitions.

Implications and Future Directions

The introduction of ARNOLD has critical implications for AI and robotics research:

• Enhanced Realism and Complexity: By simulating detailed continuous states, ARNOLD pushes the boundary for what robotic systems can achieve within simulated environments, setting a new standard for realism in robotic benchmarks.
• Promoting Robust Generalization: The benchmark explicitly aims to expose weaknesses in model generalization capabilities, which is crucial for transferring learned policies to real-world applications.
• Encouraging Integrated Approaches: There is a clear opening for novel algorithms that integrate end-to-end perception, state estimation, and control to tackle the intricate challenges posed by ARNOLD.

Looking forward, research must continue addressing the complexities introduced by continuous states and realistic simulation constraints. The work suggests potential enhancements, including the integration of more sophisticated LLMs, exploitation of larger and more varied datasets, and further reduction of the Sim2Real gap through more refined asset designs and scene variations.

In summary, ARNOLD serves as a pivotal tool in the development of future AI systems capable of nuanced, language-guided interactions with complex environments. It paves the way for novel methodologies that advance the state-of-the-art in embodied AI, particularly concerning language grounding and continuous state manipulation in robotic tasks.