Closed-Loop Visuomotor Control with Generative Expectation for Robotic Manipulation (2409.09016v3)

Abstract: Despite significant progress in robotics and embodied AI in recent years, deploying robots for long-horizon tasks remains a great challenge. Majority of prior arts adhere to an open-loop philosophy and lack real-time feedback, leading to error accumulation and undesirable robustness. A handful of approaches have endeavored to establish feedback mechanisms leveraging pixel-level differences or pre-trained visual representations, yet their efficacy and adaptability have been found to be constrained. Inspired by classic closed-loop control systems, we propose CLOVER, a closed-loop visuomotor control framework that incorporates feedback mechanisms to improve adaptive robotic control. CLOVER consists of a text-conditioned video diffusion model for generating visual plans as reference inputs, a measurable embedding space for accurate error quantification, and a feedback-driven controller that refines actions from feedback and initiates replans as needed. Our framework exhibits notable advancement in real-world robotic tasks and achieves state-of-the-art on CALVIN benchmark, improving by 8% over previous open-loop counterparts. Code and checkpoints are maintained at https://github.com/OpenDriveLab/CLOVER.

• The paper introduces CLOVER, a framework that integrates generative visual planning with explicit error quantification to enable adaptive closed-loop robotic control.
• The paper demonstrates an 8% boost on the CALVIN benchmark and a 91% improvement in long-horizon task completion over traditional open-loop systems.
• The paper’s real-time feedback and replanning method shows promise for autonomous systems, potentially advancing industrial automation with enhanced precision and adaptability.

Closed-Loop Visuomotor Control with Generative Expectation for Robotic Manipulation

Overview and Framework

The paper, "Closed-Loop Visuomotor Control with Generative Expectation for Robotic Manipulation" proposes a novel framework for robotic manipulation, named CLOVER, which integrates closed-loop control with generative models to advance visuomotor control capabilities. This method addresses the inherent limitations of existing open-loop systems that fail to adapt to real-time discrepancies during robotic tasks.

Core Components

The CLOVER framework stands on three primary pillars:

1. Reference Inputs: The system leverages a text-conditioned video diffusion model to generate future frames as reference inputs, which serve as sub-goals for the robot. The use of RGB-D video ensures a rich representation of the spatial environment and robot interaction dynamics. Optical flow regularization is applied to enhance the consistency of generated frames.
2. Error Measurement: A critical innovation in CLOVER is its explicit error quantification. The state encoder processes current observations and the synthesized sub-goals to produce compact embeddings that encapsulate the visuomotor state. The deviation between these embeddings is evaluated to provide a measure of control error.
3. Feedback-driven Controller: The closed-loop nature of CLOVER is embodied in its feedback-driven controller that iteratively plans and re-plans actions based on real-time error measurements. The system transitions between sub-goals adaptively and recalibrates if the sub-goals become infeasible due to deviations in predicted and actual states.

Numerical Results and Claims

CLOVER demonstrates superior performance on the CALVIN benchmark, improving by 8% over previous state-of-the-art open-loop counterparts. The framework's long-horizon task capabilities are validated with real-world robotic implementations, exhibiting a 91% improvement in the average length of completed tasks for long-horizon manipulation sequences. In both simulated and practical deployments, the framework shows high adaptability and robustness against background distractions and dynamic environments.

Implications and Speculative Future Developments

From a theoretical standpoint, CLOVER bridges the gap between model predictiveness and dynamic adaptability in robotic control. Its explicit error quantification and adaptive replanning capabilities could form the basis for future research in autonomous systems where real-time decision-making is crucial. Practically, the deployment of such a system in industrial automation can lead to significant advancements in precision and efficiency, reducing the need for human oversight in complex tasks involving variable environmental conditions.

The iterative refinement process evident in CLOVER ensures that the robotic system can adjust its strategies in real-time, paving the way for more sophisticated implementations of closed-loop control in AI, particularly in domains where operational contexts are highly dynamic and unpredictable.

Conclusion

CLOVER stands as a significant contribution to the field of robotic manipulation, bringing a robust mechanism through which real-time feedback can be integrated to improve the accuracy and adaptability of robotic actions. The framework's leverage of generative models for visual planning, coupled with a rigorously defined feedback loop, positions it as a promising direction for future explorations in autonomous robotic systems and AI-driven control mechanisms.