Learning Reusable Dense Rewards for Multi-Stage Robotic Manipulation Tasks

Core Concepts

A novel approach, DrS (Dense reward learning from Stages), for learning reusable dense rewards for multi-stage robotic manipulation tasks in a data-driven manner, effectively reducing human effort in reward engineering.

Abstract

The paper proposes a novel approach, DrS (Dense reward learning from Stages), for learning reusable dense rewards for multi-stage robotic manipulation tasks. The key insights are:

Leveraging the stage structure of the task, DrS learns a high-quality dense reward from sparse rewards and demonstrations (if given). The learned rewards can be reused in unseen tasks, reducing the human effort for reward engineering.

In single-stage tasks, DrS trains a discriminator to classify success and failure trajectories, using the sparse reward signal as supervision. This ensures the discriminator continues to learn meaningful information even at convergence, unlike previous adversarial imitation learning methods.

For multi-stage tasks, DrS trains a separate discriminator for each stage, where the discriminator for stage k aims to distinguish trajectories that reach beyond stage k from those that only reach up to stage k. The stage-specific discriminators are then combined to form the final dense reward.

Extensive experiments on 1,000+ task variants from three physical robot manipulation task families (Pick-and-Place, Turn Faucet, Open Cabinet Door) demonstrate that the learned rewards can be reused in unseen tasks, resulting in improved performance and sample efficiency of RL algorithms compared to using sparse rewards. In certain tasks, the learned rewards even achieve comparable performance to human-engineered reward functions.

The proposed approach significantly reduces the human effort required for reward engineering. For example, while the human-engineered reward for "Open Cabinet Door" involves over 100 lines of code, 10 candidate terms, and tons of "magic" parameters, DrS only requires two boolean functions as stage indicators.

Stats

The object is in close proximity to the goal position.
The robot arm and the object remain stationary.
The handle reaches a target angle.
The door is opened to a sufficient degree and remains stationary.

Quotes

"The success of many reinforcement learning (RL) techniques heavily relies on dense reward functions, which are often tricky to design by humans due to heavy domain expertise requirements and tedious trials and errors."
"Ideally, the learned reward will be reused to efficiently solve new tasks that share similar success conditions with the task used to learn the reward."
"Our approach involves incorporating sparse rewards as a supervision signal in lieu of the original signal used for classifying demonstration and agent trajectories."

Key Insights Distilled From

DrS: Learning Reusable Dense Rewards for Multi-Stage Tasks

by Tongzhou Mu,... at arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16779.pdf

Deeper Inquiries

How can the stage structure of a task be automatically inferred or learned, rather than relying on manually defined stage indicators

To automatically infer or learn the stage structure of a task, one could leverage techniques from unsupervised learning, reinforcement learning, or even natural language processing.

Unsupervised Learning: By analyzing the state transitions and patterns in the data, clustering algorithms could be used to identify recurring patterns that indicate different stages of a task. Dimensionality reduction techniques like PCA or t-SNE could also help in visualizing the data and potentially revealing underlying stage structures.

Reinforcement Learning: Reinforcement learning algorithms could be employed to learn the optimal policy for a task. By observing the agent's behavior and the rewards it receives, one could infer the different stages based on the agent's actions and the corresponding outcomes.

Natural Language Processing: If the task descriptions or instructions are available in textual form, language models could be used to extract information about the task structure. By analyzing the language used to describe the task, one could potentially identify different stages or subtasks implicitly mentioned in the text.

By combining these approaches and potentially incorporating domain-specific knowledge or heuristics, it may be possible to automatically infer or learn the stage structure of a task without relying on manually defined stage indicators.

What are the potential limitations or failure modes of the proposed approach when dealing with highly complex or ambiguous multi-stage tasks

The proposed approach may face limitations or failure modes when dealing with highly complex or ambiguous multi-stage tasks. Some potential challenges include:

Ambiguity in Stage Definitions: In tasks where the boundaries between stages are not well-defined or where the stages overlap, the learned rewards may not accurately capture the progression through the task. This ambiguity could lead to suboptimal performance or confusion for the RL agent.

Curse of Dimensionality: In highly complex tasks with a large state space, the learned rewards may struggle to capture all relevant information or nuances of the task. This could result in sparse rewards that do not effectively guide the agent towards the desired goal.

Generalization to Unseen Tasks: The approach's ability to generalize to unseen tasks within the same task family may be limited if the learned rewards are too specific to the training tasks. This could lead to poor performance or the need for extensive fine-tuning when applied to new tasks.

Overfitting to Training Data: If the training data does not adequately represent the variability and complexity of the task, the learned rewards may overfit to the training set, leading to suboptimal performance on unseen tasks.

Addressing these limitations may require additional techniques such as regularization, transfer learning, or more sophisticated reward shaping strategies to ensure robust performance in diverse multi-stage tasks.

Could the learned dense rewards be further improved by incorporating additional information beyond the sparse reward and stage indicators, such as task-specific domain knowledge or language-based descriptions of the task

The learned dense rewards could potentially be further improved by incorporating additional information beyond the sparse reward and stage indicators. Some ways to enhance the learned rewards include:

Task-Specific Domain Knowledge: Integrating domain-specific knowledge or constraints into the reward function could help capture task intricacies that are not explicitly captured by the sparse reward or stage indicators. This could lead to more nuanced and effective rewards tailored to the specific task requirements.

Language-Based Descriptions: Utilizing natural language descriptions of the task to inform the reward learning process could provide additional context and semantics that are not present in the sparse reward or stage indicators. Language models could be used to extract relevant information and incorporate it into the reward function.

Hierarchical Reward Structures: Designing a hierarchical reward structure that combines information from the sparse reward, stage indicators, domain knowledge, and language-based descriptions could lead to a more comprehensive and informative reward signal. By hierarchically organizing the reward components, the agent could receive feedback at different levels of abstraction, enhancing its learning process.

By integrating these additional sources of information, the learned dense rewards could be enriched and refined, leading to improved performance and adaptability across a wider range of tasks.