Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models (2310.10639v1)

Abstract: If generalist robots are to operate in truly unstructured environments, they need to be able to recognize and reason about novel objects and scenarios. Such objects and scenarios might not be present in the robot's own training data. We propose SuSIE, a method that leverages an image-editing diffusion model to act as a high-level planner by proposing intermediate subgoals that a low-level controller can accomplish. Specifically, we finetune InstructPix2Pix on video data, consisting of both human videos and robot rollouts, such that it outputs hypothetical future "subgoal" observations given the robot's current observation and a language command. We also use the robot data to train a low-level goal-conditioned policy to act as the aforementioned low-level controller. We find that the high-level subgoal predictions can utilize Internet-scale pretraining and visual understanding to guide the low-level goal-conditioned policy, achieving significantly better generalization and precision than conventional language-conditioned policies. We achieve state-of-the-art results on the CALVIN benchmark, and also demonstrate robust generalization on real-world manipulation tasks, beating strong baselines that have access to privileged information or that utilize orders of magnitude more compute and training data. The project website can be found at http://rail-berkeley.github.io/susie .

Analysis of "Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models"

The paper "Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models" introduces SuSIE, a method designed to enhance robotic manipulation tasks in unexplored environments using minimal task-specific data. The core contribution of this paper is the utilization of image-editing diffusion models, specifically finetuning the InstructPix2Pix model, to formulate robotic subgoals based on visual and language information, leading to notably improved robotic control without explicit task data.

Summary of Method and Results

SuSIE leverages a two-fold approach: a high-level planner which predicts intermediate subgoals using pretrained image-editing models, followed by a low-level goal-conditioned policy to translate these subgoals into executable actions. This hierarchical strategy facilitates better semantic understanding and precision in guiding robots through unfamiliar tasks and environments. Crucially, the paper reports that SuSIE leads to superior performance on the CALVIN benchmark and matches or outperforms several existing methods, including state-of-the-art approaches like RT-2-X, across various real-world robotic manipulation tasks.

The paper delineates experimental evaluations in both simulated (CALVIN) and real-world environments, illustrating SuSIE’s effectiveness in both settings. In simulation, SuSIE demonstrates that it outperforms prior methods in chaining tasks from multiple environments, achieving a higher average success rate. In real-world tasks involving multiple scenes with both familiar and novel objects, SuSIE consistently achieves better generalization and precision relative to baseline methods, even surpassing the performance of models trained with considerably more data.

Implications and Future Directions

The paper's findings suggest significant implications for the design and deployment of generalist robots in real-world scenarios. By improving the semantic comprehension and execution precision in open-world settings, SuSIE opens potential exploration for broader applications in complex environments not seen during training. The approach of leveraging internet-scale pretrained models proves beneficial not only for understanding and navigating novel objects and scenarios but also for operational efficiency in robotic control.

Future research could examine integrating policy-aware subgoal generation that considers the capabilities of the low-level controller, potentially enhancing the synergy between the configured model stages. Additionally, expanding this framework to incorporate multimodal data sources beyond images and language, such as tactile or auditory cues, may further enrich the robot's interaction dynamics. Refinements in adaptive control methods, aimed at extending beyond vision-centered models, represent a promising research frontier sparked by this work.

In conclusion, the proposed method facilitates a robust paradigm for enabling zero-shot capability in robotics through sophisticated use of pretrained diffusion models, marking a valuable contribution in the field of robotic manipulation and AI.