Reflective Planning: Vision-Language Models for Multi-Stage Long-Horizon Robotic Manipulation (2502.16707v1)

Abstract: Solving complex long-horizon robotic manipulation problems requires sophisticated high-level planning capabilities, the ability to reason about the physical world, and reactively choose appropriate motor skills. Vision-LLMs (VLMs) pretrained on Internet data could in principle offer a framework for tackling such problems. However, in their current form, VLMs lack both the nuanced understanding of intricate physics required for robotic manipulation and the ability to reason over long horizons to address error compounding issues. In this paper, we introduce a novel test-time computation framework that enhances VLMs' physical reasoning capabilities for multi-stage manipulation tasks. At its core, our approach iteratively improves a pretrained VLM with a "reflection" mechanism - it uses a generative model to imagine future world states, leverages these predictions to guide action selection, and critically reflects on potential suboptimalities to refine its reasoning. Experimental results demonstrate that our method significantly outperforms several state-of-the-art commercial VLMs as well as other post-training approaches such as Monte Carlo Tree Search (MCTS). Videos are available at https://reflect-vlm.github.io.

Reflective Planning: Vision-LLMs for Multi-Stage Long-Horizon Robotic Manipulation

In the presented paper, the authors address the significant challenges associated with complex long-horizon robotic manipulation tasks by proposing a novel method that enhances the physical reasoning capabilities of Vision-LLMs (VLMs). These tasks pose considerable difficulty as they require sophisticated planning capabilities that involve reasoning over intricate physical interactions and their consequences over extended time horizons. Traditional approaches such as Task and Motion Planning (TAMP) are often limited in applicability when rich visual information is involved, prompting the need for more adaptable solutions.

The core innovation in this paper lies in the introduction of a "reflection" mechanism integrated within a VLM framework. The authors implement a test-time computation methodology that iteratively enhances the decision-making process of a pretrained VLM by imagining future world states with the help of a generative model. By utilizing a diffusion-based dynamics model, their approach can predict potential future states resulting from executed action sequences. This foresight enables VLMs to critique and refine their initial plans, thereby improving overall action selection and execution.

Experimental evaluation demonstrated that this reflective planning method significantly outperforms contemporary state-of-the-art VLMs and other established methodologies like Monte Carlo Tree Search (MCTS) across complex multi-stage robotic tasks. Importantly, these state-of-the-art models showed limitations in handling the nuanced physics and long-horizon planning required in the test scenarios, with the novel reflective approach offering notable improvements.

The implications of this research are manifold, both in practical robotics applications and theoretical advancements in AI. Practically, the method offers a promising strategy for enhancing VLM capabilities in physically grounded tasks without necessitating extensive retraining or large-scale data acquisition. Theoretically, it provides insights into integrating structured reasoning mechanisms with large-scale pretrained models, furthering the understanding of how these systems can be adapted to complex environmental interactions.

In terms of future developments, advancements in generative models may further bolster the reflection mechanism's ability to accurately predict and evaluate longer sequences of action outcomes. This holds the potential to expand the applicability of this method to a broader range of problems in robotics and autonomous systems, particularly those necessitating a deep understanding of physical reasoning and sequenced decision-making.

In summary, the proposed reflective planning framework represents a significant step towards enabling VLMs to tackle complex robotic manipulation tasks effectively. By combining visual prediction with iterative refinement, the authors provide a robust approach that enhances the decision-making ability of VLMs while maintaining computational efficiency, suggesting broader applicability and potential in robotics and related fields.