From Play to Policy: Conditional Behavior Generation from Uncurated Robot Data (2210.10047v3)

Abstract: While large-scale sequence modeling from offline data has led to impressive performance gains in natural language and image generation, directly translating such ideas to robotics has been challenging. One critical reason for this is that uncurated robot demonstration data, i.e. play data, collected from non-expert human demonstrators are often noisy, diverse, and distributionally multi-modal. This makes extracting useful, task-centric behaviors from such data a difficult generative modeling problem. In this work, we present Conditional Behavior Transformers (C-BeT), a method that combines the multi-modal generation ability of Behavior Transformer with future-conditioned goal specification. On a suite of simulated benchmark tasks, we find that C-BeT improves upon prior state-of-the-art work in learning from play data by an average of 45.7%. Further, we demonstrate for the first time that useful task-centric behaviors can be learned on a real-world robot purely from play data without any task labels or reward information. Robot videos are best viewed on our project website: https://play-to-policy.github.io

Conditional Behavior Generation from Uncurated Robot Data

The paper "From Play to Policy: Conditional Behavior Generation from Uncurated Robot Data" introduces Conditional Behavior Transformers (C-BeT), an approach aimed at improving the capability of robotic systems to learn task-centric behaviors from uncurated, offline datasets, often referred to as "play data." This work addresses significant challenges in applying large-scale sequence modeling advancements from domains like language and vision generation to robotics, especially given the noisy and multi-modal nature of play data collected through non-expert human interactions.

Methodological Overview

The core innovation of C-BeT lies in its architecture, which integrates the strength of Behavior Transformers (BeT) for handling multi-modal behavior cloning with a novel future-conditioned goal specification. This is designed to transform general play data into executable, task-specific policies without relying on additional human annotations, reward signals, or online retraining phases.

Key Contributions and Experimental Findings

1. Generative Transformer Approach: C-BeT leverages a transformer-based architecture for generating conditional behaviors. Unlike traditional behavior cloning models that assume unimodal distributions, C-BeT models a potential multi-modal distribution of actions, which is critical for handling the diversity in play data.
2. Play Data Utilization: The methodology focuses on leveraging unannotated, reward-free datasets by dynamically conditioning on desired future outcomes. This conditioning is achieved by inferring goals from future states within the trajectories.
3. Performance Improvement: Experimental evaluations across several simulated benchmarks (including CARLA for autonomous driving simulations, multi-modal block-pushing tasks, and a simulated kitchen environment) demonstrated the superiority of C-BeT. It achieved a substantial average performance improvement of 45.7% over existing state-of-the-art methods.
4. Real-World Application: Notably, C-BeT has been validated using a real-world robotic setup involving a Franka Emika Panda robot interacting with a toy kitchen environment. It showcases the learning of effective visual policies purely from the unstructured play data, achieving successful task completion in a variety of scenarios without task-specific labeling.
5. Adaptability and Generalization: The model proves capable of generalizing across different task conditions and variable environments, demonstrating robustness in novel conditions and presence of environment distractors—a significant step towards adaptable, real-world robotic applications.

Implications and Future Directions

C-BeT's ability to learn from uncurated datasets without requiring precise reward function specification opens new possibilities for scalable robot learning applications. The proposed approach exemplifies an efficient mechanism for transitioning from data-rich, unstructured environments to structured policy execution, allowing robots to autonomously infer and execute desired tasks.

However, some limitations pointed out include challenges in representation learning, particularly when dealing with specific object interactions (e.g., knob manipulation), suggesting that future research could focus on refining visual and proprioceptive representations to enhance task-specific performance further. Additionally, C-BeT's reliance on extensive datasets hints at future research opportunities in optimizing data efficiency and representation learning strategies, perhaps through improved data augmentation techniques or integrating more sophisticated self-supervised learning frameworks.

Overall, C-BeT presents a significant advancement in the field of robot learning from uncurated data and lays groundwork for subsequent developments in autonomous, scalable robot systems capable of operating effectively in complex, real-world environments.