Steering Your Generalists: Improving Robotic Foundation Models via Value Guidance

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General-purpose robotic policies are becoming increasingly popular, but they often fail due to imprecise manipulation, especially when encountering environmental distribution shifts. This paper proposes a novel approach called Value-Guided Policy Steering (V-GPS) to improve the performance of generalist robot policies by re-ranking their actions at deployment time based on a value function learned via offline RL.

Key Idea: V-GPS learns a value function that estimates the expected return of taking an action in a given state. This value function can then be used to re-rank the actions proposed by a generalist policy, selecting the actions that are most likely to lead to a successful outcome.

Concrete Example: Imagine a robot tasked with “putting a green pepper in a pot.” A generalist policy might grasp the pepper but drop it before reaching the pot, or it might grasp the pepper incorrectly and fumble it. V-GPS can improve this by re-ranking the actions proposed by the generalist policy, prioritizing those that lead to a secure grasp and a smooth trajectory to the pot.

How it Works:

1. Offline RL Training: V-GPS trains a value function using offline RL data, which consists of demonstrations of robot actions along with their corresponding rewards. The reward is typically set to +1 for successful task completion and 0 otherwise.

2. Test-Time Action Re-Ranking: At deployment time, the generalist policy proposes a set of actions for the current state. V-GPS then re-ranks these actions based on their estimated value and selects the highest-ranking action for execution.

Benefits:

• Plug-and-Play: V-GPS works with any off-the-shelf generalist policy, without needing to fine-tune or even access the policy’s weights.
• Consistent Improvement: V-GPS consistently improves the performance of five different state-of-the-art policies across multiple robotic platforms and tasks.
• Real-World Results: V-GPS achieves an improvement of +82% in real-world manipulation tasks, demonstrating its effectiveness in real-world scenarios.

Future Work:

The authors discuss limitations, such as the reliance on a single value function trained on limited data, and propose scaling up the value function architecture and using more diverse data to improve generalizability.

This research opens up new possibilities for improving the performance of generalist robot policies, potentially leading to more robust and versatile robots capable of handling a wider range of tasks in real-world settings.

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