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RoboReflect: A Robotic Reflective Reasoning Framework for Grasping Ambiguous-Condition Objects

Zhen Luo, Yixuan Yang, Yanfu Zhang, Feng Zheng

TL;DR

RoboReflect tackles grasping ambiguous-condition objects by combining autonomous reflective reasoning with a memory-augmented framework powered by large vision-language models. It decomposes the task into four modules—vision/action planning, judgment, reflective reasoning (self-reflection plus discussion), and memory—to autonomously detect, analyze, and correct grasp errors until success. The approach demonstrates superior performance over baselines (AnyGrasp, ReKep, GPT-4V) across eight objects, with notable gains from the memory and discussion components. This work highlights the importance of autonomous self-reflection and memory in enabling resilient and adaptable robotic manipulation in complex real-world environments.

Abstract

As robotic technology rapidly develops, robots are being employed in an increasing number of fields. However, due to the complexity of deployment environments or the prevalence of ambiguous-condition objects, the practical application of robotics still faces many challenges, leading to frequent errors. Traditional methods and some LLM-based approaches, although improved, still require substantial human intervention and struggle with autonomous error correction in complex scenarios. In this work, we propose RoboReflect, a novel framework leveraging large vision-language models (LVLMs) to enable self-reflection and autonomous error correction in robotic grasping tasks. RoboReflect allows robots to automatically adjust their strategies based on unsuccessful attempts until successful execution is achieved. The corrected strategies are saved in the memory for future task reference. We evaluate RoboReflect through extensive testing on eight common objects prone to ambiguous conditions of three categories. Our results demonstrate that RoboReflect not only outperforms existing grasp pose estimation methods like AnyGrasp and high-level action planning techniques ReKep with GPT-4V but also significantly enhances the robot's capability to adapt and correct errors independently. These findings underscore the critical importance of autonomous self-reflection in robotic systems while effectively addressing the challenges posed by ambiguous-condition environments.

RoboReflect: A Robotic Reflective Reasoning Framework for Grasping Ambiguous-Condition Objects

TL;DR

RoboReflect tackles grasping ambiguous-condition objects by combining autonomous reflective reasoning with a memory-augmented framework powered by large vision-language models. It decomposes the task into four modules—vision/action planning, judgment, reflective reasoning (self-reflection plus discussion), and memory—to autonomously detect, analyze, and correct grasp errors until success. The approach demonstrates superior performance over baselines (AnyGrasp, ReKep, GPT-4V) across eight objects, with notable gains from the memory and discussion components. This work highlights the importance of autonomous self-reflection and memory in enabling resilient and adaptable robotic manipulation in complex real-world environments.

Abstract

As robotic technology rapidly develops, robots are being employed in an increasing number of fields. However, due to the complexity of deployment environments or the prevalence of ambiguous-condition objects, the practical application of robotics still faces many challenges, leading to frequent errors. Traditional methods and some LLM-based approaches, although improved, still require substantial human intervention and struggle with autonomous error correction in complex scenarios. In this work, we propose RoboReflect, a novel framework leveraging large vision-language models (LVLMs) to enable self-reflection and autonomous error correction in robotic grasping tasks. RoboReflect allows robots to automatically adjust their strategies based on unsuccessful attempts until successful execution is achieved. The corrected strategies are saved in the memory for future task reference. We evaluate RoboReflect through extensive testing on eight common objects prone to ambiguous conditions of three categories. Our results demonstrate that RoboReflect not only outperforms existing grasp pose estimation methods like AnyGrasp and high-level action planning techniques ReKep with GPT-4V but also significantly enhances the robot's capability to adapt and correct errors independently. These findings underscore the critical importance of autonomous self-reflection in robotic systems while effectively addressing the challenges posed by ambiguous-condition environments.
Paper Structure (14 sections, 1 equation, 4 figures, 3 tables)

This paper contains 14 sections, 1 equation, 4 figures, 3 tables.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. METHODOLOGY
  4. Problem Statement
  5. Converting RGB-D images & Action Planning
  6. Status Determining by Judgment Module
  7. Correction by Reflective Reasoning Module
  8. Memory Module
  9. EXPERIMENTS
  10. Experimental Setup
  11. Overall Performance
  12. Reflect Analysis
  13. Ablation Study
  14. CONCLUSION

Figures (4)

  • Figure 1: The RoboReflect Framework for Autonomous Error Correction in Robotic Grasping Tasks. The process begins with the Vision Processing Module, which extracts RGB $\bm{I}$ and depth images $\bm{I_D}$, generating 3D spatial positions $\bm{S}$. $\bm{I}$ pass to the LVLM $\mathcal{M}$, which, along with text instructions $\bm{Ins}$, generates actions $\bm{I_{act}}$ through the Action Module. The Judgment Module evaluates the grasp based on the grasp state $\bm{G_S}$ and grasp position $\bm{G_P}$, using action images $\bm{I_{act}}$,$\bm{S}$, and $\bm{Ins}$. If the grasp fails (i.e., $\bm{G_S} \cup \bm{G_P} = 0$), the system engages the Reflective Reasoning Module, which analyzes the error and proposes corrective suggestions $\bm{R'}$ through reasoning steps. The memory module stores object descriptions and successful grasp strategies to enhance future grasping attempts, ensuring continuous improvement. If the grasp is successful (i.e., $\bm{G_S} \cup \bm{G_P} = 1$), the correct strategy is saved in memory for future reference.
  • Figure 2: Objects descriptions. The upper half of tissue bag is often empty due to the exhaustion of materials, which makes them susceptible to deformation. A closed-lid cup has a securely fastened lid. An open-lid cup risks lid-body separation due to loose closure. Cookies are fragile and breakable. The upper half of a hard drive is labeled Untouchable. Sealed cup noodles denote standard intact packaging. Unsealed cup noodles have vulnerable tops prone to spills/deformation. The edible portion of an ice cream bar must remain untouched.
  • Figure 3: Visual comparison of grasping posture. The green ones are our method, the blue poses are the result of ReKep, and the red ones are the result of AnyGrasp.
  • Figure 4: The comparison between failed (without reflect) and successful (with reflect) grasping cases. We present the grasping results of six objects before and after using our RoboReflect model. Before reflection, the grasping attempts often failed due to an inability to understand the object's properties, resulting in either a failure to grasp or an incorrect grasp position. After reflection, the model was able to successfully grasp the objects.