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Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials (2210.05178v3)

Published 11 Oct 2022 in cs.RO and cs.LG

Abstract: Progress in deep learning highlights the tremendous potential of utilizing diverse robotic datasets for attaining effective generalization and makes it enticing to consider leveraging broad datasets for attaining robust generalization in robotic learning as well. However, in practice, we often want to learn a new skill in a new environment that is unlikely to be contained in the prior data. Therefore we ask: how can we leverage existing diverse offline datasets in combination with small amounts of task-specific data to solve new tasks, while still enjoying the generalization benefits of training on large amounts of data? In this paper, we demonstrate that end-to-end offline RL can be an effective approach for doing this, without the need for any representation learning or vision-based pre-training. We present pre-training for robots (PTR), a framework based on offline RL that attempts to effectively learn new tasks by combining pre-training on existing robotic datasets with rapid fine-tuning on a new task, with as few as 10 demonstrations. PTR utilizes an existing offline RL method, conservative Q-learning (CQL), but extends it to include several crucial design decisions that enable PTR to actually work and outperform a variety of prior methods. To our knowledge, PTR is the first RL method that succeeds at learning new tasks in a new domain on a real WidowX robot with as few as 10 task demonstrations, by effectively leveraging an existing dataset of diverse multi-task robot data collected in a variety of toy kitchens. We also demonstrate that PTR can enable effective autonomous fine-tuning and improvement in a handful of trials, without needing any demonstrations. An accompanying overview video can be found in the supplementary material and at thi URL: https://sites.google.com/view/ptr-final/

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Summary

  • The paper demonstrates that PTR leverages offline RL pre-training to enable fast adaptation of robotic policies with minimal demonstrations.
  • It integrates high-capacity neural networks, precise normalization, and optimized action embedding to enhance performance over traditional methods.
  • Empirical results on skill re-targeting and domain adaptation show PTR significantly outperforms behavioral cloning and standard RL approaches.

Pre-Training for Robots: Offline RL Enables Learning New Tasks in a Handful of Trials

The paper presents an innovative framework named Pre-Training for Robots (PTR) that utilizes offline reinforcement learning (RL) to enable rapid learning of new robotic tasks with minimal trial-and-error. This novel approach addresses the challenge of efficiently leveraging diverse multi-task datasets to pre-train robotic policies, facilitating quick adaptation to new tasks in unfamiliar environments.

Technical Approach

PTR capitalizes on prior data by first performing offline RL pre-training on extensive multi-task datasets, which are cautiously fine-tuned using a minimal number of demonstrations for new target tasks. The method extends conservative Q-learning (CQL)—an existing offline RL algorithm—by integrating critical design choices that enhance performance on real robotic platforms. These choices include adopting high-capacity neural network architectures, precise normalization techniques, and mechanisms for action embedding, which collectively underpin the success of PTR.

Empirical Validation

The framework's effectiveness is demonstrated through several real-world scenarios, encompassing skill re-targeting, domain adaptation, and new task learning in unseen domains. Empirical results exhibit PTR's superior performance compared to traditional behavioral cloning strategies, joint training schemes, and advanced visual pre-training methods.

  1. Skill Re-targeting: In an experiment where the robot needed to adapt the "put sushi in a pot" skill to a different pot, PTR achieved a success rate of 46.67%, outperforming behavioral cloning and other RL methods that struggled with adapting to new objects.
  2. Domain Adaptation: When tasked with opening a previously unseen microwave door, PTR demonstrated a significant success rate of 60%, surpassing other methods like Behavioral Cloning (BC) and CQL that were less effective in generalizing to new domains.
  3. New Task Learning: PTR showed marked improvements in learning tasks such as object placement and sorting in fresh environments. It continually outperformed algorithms that focused solely on offline pre-training without task-specific fine-tuning.

Design Choices and System Enhancements

The paper emphasizes key architectural decisions that are critical for PTR's success:

  • High-Capacity Networks: Utilizing ResNet architectures with group normalization and learned spatial embeddings provided the necessary model capacity to manage the complexity of multi-task datasets.
  • Optimized Action Embedding: Incorporating actions into various layers of the Q-network ensured better learning dynamics and avoided the pitfalls of flawed action-value predictions in narrow demonstration scenarios.
  • Balanced Training Regimes: Mixing pre-training data with a small fraction of target task data during fine-tuning facilitated a better learning process, enhancing the robot's adaptation capabilities.

Implications and Future Directions

The research opens new opportunities for utilizing large-scale offline data to pre-train robotic systems integrated with efficient learning paradigms. This could lead to the development of general-purpose robotic agents capable of rapid task adaptation with minimal human intervention. PTR could influence advancements in autonomous systems by providing a standard for initial policy learning, leveraging the benefits of offline datasets for reliable and scalable robotic operations.

Researchers might build on this foundation by exploring the scalability of PTR to more complex robotic interactions and fine-tuning dynamics across heterogeneous environments. Additionally, combining the advantages of multi-task learning with vision-based pre-training approaches could further enhance the adaptability and reliability of robotic systems in diverse operational settings.

Overall, the paper contributes significant insights into the domain of offline RL for robotic applications, illustrating the practical viability and theoretical robustness of PTR in learning efficient robotic policies from extensive pre-training data.

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