Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation (2312.13139v2)

Abstract: Generative pre-trained models have demonstrated remarkable effectiveness in language and vision domains by learning useful representations. In this paper, we extend the scope of this effectiveness by showing that visual robot manipulation can significantly benefit from large-scale video generative pre-training. We introduce GR-1, a straightforward GPT-style model designed for multi-task language-conditioned visual robot manipulation. GR-1 takes as inputs a language instruction, a sequence of observation images, and a sequence of robot states. It predicts robot actions as well as future images in an end-to-end manner. Thanks to a flexible design, GR-1 can be seamlessly finetuned on robot data after pre-trained on a large-scale video dataset. We perform extensive experiments on the challenging CALVIN benchmark and a real robot. On CALVIN benchmark, our method outperforms state-of-the-art baseline methods and improves the success rate from 88.9% to 94.9%. In the setting of zero-shot unseen scene generalization, GR-1 improves the success rate from 53.3% to 85.4%. In real robot experiments, GR-1 also outperforms baseline methods and shows strong potentials in generalization to unseen scenes and objects. We provide inaugural evidence that a unified GPT-style transformer, augmented with large-scale video generative pre-training, exhibits remarkable generalization to multi-task visual robot manipulation. Project page: https://GR1-Manipulation.github.io

Overview of "Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation"

The paper presents a significant exploration into leveraging large-scale video generative pre-training to enhance the performance of visual robot manipulation tasks. The authors introduce a model named GR-1, an extension of the Generative Pre-trained Transformer (GPT) architecture, customized for handling language-conditioned multi-task visual robot manipulation. This research capitalizes on the paradigm that video data, owing to its sequential and predictive nature, can effectively inform learning processes used in robotics.

Methodology

GR-1 employs a unified approach for representing language instructions, observation images, and robot states as inputs while predicting robot actions and future video frames as outputs. The architecture integrates a causal transformer model, which is pre-trained using video data from the Ego4D dataset, a large-scale collection featuring extensive human-object interactions annotated with language descriptions.

The paper emphasizes the use of video prediction as a step towards effective action prediction. This is based on the rationale that the ability to predict visual outcomes can guide a robot in anticipating the results of its actions—integrating principles well established in sequential decision-making processes.

During empirical evaluations, the model's efficacy was tested on the CALVIN benchmark, which poses a challenging environment with multiple tasks requiring language-conditioned manipulation. Additionally, the model's performance was tested on real-world robotic tasks focusing on object transportation and articulated object manipulations.

Key Findings

1. Improved Performance on Multi-Task Learning: In tests performed using the CALVIN benchmark, GR-1 showcased superior task completion rates compared to existing models. Specific success was noted in the ability to handle long sequences of tasks (up to 5 in a row), significantly surpassing the baseline models such as RT-1 and multi-task variations of state-of-the-art pre-trained models like R3M.
2. Zero-Shot Generalization: GR-1 demonstrated substantial improvements in zero-shot generalization capabilities, particularly in unseen environments and with unseen language instructions. This highlights the model's ability to leverage its pre-trained representations to adapt to new and previously unencountered conditions.
3. Data Efficiency: The experiments revealed GR-1's capacity to achieve high levels of performance even when trained with only 10% of the available dataset, indicating superior data efficiency—a critical advantage given the cost and complexity associated with real-world robotics data collection.
4. Real-World Application: The paper also underscores GR-1's applicability in real-world settings. In object transportation and articulated manipulation tasks with a Kinova robot, it exhibited robust performance and generalization to unseen object instances and categories—a testament to its practical utility.

Implications and Future Directions

This paper has substantial implications for the field of robotic learning, particularly in capitalizing on large-scale datasets that are not originally intended for robotics. The success of GR-1 suggests a promising direction towards models that can generalize across diverse tasks, environments, and instructions, reducing reliance on task-specific data.

For theoretical advancements, the work exemplifies how combining generative pre-training with traditional reinforcement signals can bolster generalization capabilities. Practically, this approach can drive the development of versatile robotic systems capable of adapting to a plethora of environments, with meaningful potential applications in industries such as surveillance, logistics, and personalized robotics.

Future research could explore the marriage of even broader datasets, incorporating synthetic simulations or leveraging transfer learning from adjacent domains like navigation or complex planning systems. Additionally, it remains to be seen how such models fare with other modalities or whether incorporating physical interactions could further optimize performance.

The collaborative pursuit of enhancing robot learning with substantial pre-training sets a novel precedent in robotic research, promising more adaptable and intelligent robotic systems in the near horizon.