ClevrSkills: Compositional Language and Visual Reasoning in Robotics (2411.09052v1)

Abstract: Robotics tasks are highly compositional by nature. For example, to perform a high-level task like cleaning the table a robot must employ low-level capabilities of moving the effectors to the objects on the table, pick them up and then move them off the table one-by-one, while re-evaluating the consequently dynamic scenario in the process. Given that large vision LLMs (VLMs) have shown progress on many tasks that require high level, human-like reasoning, we ask the question: if the models are taught the requisite low-level capabilities, can they compose them in novel ways to achieve interesting high-level tasks like cleaning the table without having to be explicitly taught so? To this end, we present ClevrSkills - a benchmark suite for compositional reasoning in robotics. ClevrSkills is an environment suite developed on top of the ManiSkill2 simulator and an accompanying dataset. The dataset contains trajectories generated on a range of robotics tasks with language and visual annotations as well as multi-modal prompts as task specification. The suite includes a curriculum of tasks with three levels of compositional understanding, starting with simple tasks requiring basic motor skills. We benchmark multiple different VLM baselines on ClevrSkills and show that even after being pre-trained on large numbers of tasks, these models fail on compositional reasoning in robotics tasks.

• The paper introduces ClevrSkills, a benchmark featuring 33 robotic tasks and 330K trajectories to evaluate compositional reasoning in vision-language models.
• It details a novel environment framework built on ManiSkill2 that incorporates multi-modal annotations for imitation and reinforcement learning.
• Benchmark analysis reveals that while current models excel in simple motor tasks, they struggle with complex sequential tasks, guiding future architectural improvements.

Compositional Language and Visual Reasoning in Robotics: An Examination of ClevrSkills

The paper "ClevrSkills: Compositional Language and Visual Reasoning in Robotics" presents an innovative approach to understanding compositional reasoning in robotics tasks. The objective lies in evaluating whether vision LLMs (VLMs), which exhibit remarkable human-like reasoning abilities, can extend their performance to intricate robotic tasks without explicit training. The research unveils the ClevrSkills benchmark, designed as a robust environment suite for analyzing compositional understanding in robotics models, constructed atop the ManiSkill2 simulation platform.

Key Contributions

1. Environment Framework: The authors introduce ClevrSkills, comprising 33 tasks distributed across three complexity levels, to serve as a compositional reasoning benchmark. It utilizes multi-modal prompts for task specifications, contrasting with existing isolated abstract planning evaluations by including both high and low-level execution layers.
2. Dataset Creation: A novel dataset featuring 330k ground truth trajectories is provided, useful for imitation learning. This dataset contains diverse annotations, including language directives, bounding boxes, key-steps, and rewards designed for offline reinforcement learning (RL), essential for robust learning and deployment of robotic tasks.
3. Benchmark Analysis: By benchmarking state-of-the-art (SOTA) vision LLMs, the paper reveals existing challenges in compositional understanding, highlighting models' ineffectiveness in executing tasks necessitating multi-layered reasoning.

Numerical Results and Model Performance

The empirical evaluation underscores the capability gap of large VLMs when tasked with compositional understanding in robotics. Examined models, including JAT, RoboFlamingo, Octo, and StreamRoboLM, display moderate success in isolated motor tasks (L0) but struggle with compound and higher-order tasks (L1 and L2). The StreamRoboLM models, particularly the OPT and Llama versions, demonstrate a higher success rate at the L0 level compared to alternatives, achieving figures around 62.5%. However, the inability to extend these achievements to complex compositions (L1 and L2) highlights deficiencies in current architectures when generalizing learned skills zero-shot beyond fixed libraries of basic motor skills.

Implications and Future Directions

The findings from the ClevrSkills benchmark lead to several theoretical and practical implications for robotics and AI research. The apparent challenge posed by compound tasks calls for more nuanced network architectures capable of dynamic adaptation and compositional reasoning in unstructured environments. The integration of multi-modal data and fine-grained linguistic annotations in datasets such as ClevrSkills offers a promising avenue for refining multi-modal learning algorithms, fostering models capable of exhibiting more profound cognitive emulation.

Future research should aim to mitigate the limitations noted by expanding to real-world datasets and addressing long-range task abstractions to enhance the representation of robotic reasoning. Additionally, diversifying robotic embodiments within the benchmark may yield insights into architecture-specific performance and generalization across variable mechanical platforms.

Conclusion

The introduction of ClevrSkills marks a significant step in compositional reasoning within the robotics domain. While current VLMs excel in discrete functionalities, their leap towards comprehensive multi-level reasoning remains an open challenge, evidenced by the benchmark results. Progress in this arena is crucial for advancing autonomous systems capable of seamlessly integrating into human-centric environments, showcasing intelligent adaptability across unprecedented tasks. The continued evolution of benchmarks like ClevrSkills will undeniably drive innovation in robotics AI, shaping the future competency of autonomous agents.