Policy Adaptation via Language Optimization: Decomposing Tasks for Few-Shot Imitation (2408.16228v1)

Abstract: Learned language-conditioned robot policies often struggle to effectively adapt to new real-world tasks even when pre-trained across a diverse set of instructions. We propose a novel approach for few-shot adaptation to unseen tasks that exploits the semantic understanding of task decomposition provided by vision-LLMs (VLMs). Our method, Policy Adaptation via Language Optimization (PALO), combines a handful of demonstrations of a task with proposed language decompositions sampled from a VLM to quickly enable rapid nonparametric adaptation, avoiding the need for a larger fine-tuning dataset. We evaluate PALO on extensive real-world experiments consisting of challenging unseen, long-horizon robot manipulation tasks. We find that PALO is able of consistently complete long-horizon, multi-tier tasks in the real world, outperforming state of the art pre-trained generalist policies, and methods that have access to the same demonstrations.

• The paper introduces PALO, which decomposes high-level tasks into manageable subtasks to enable effective few-shot policy adaptation.
• It employs a nonparametric adaptation method by calibrating task decompositions with a limited set of demonstrations instead of extensive fine-tuning.
• Empirical results show that PALO significantly outperforms methods like Octo and RT-2-X, achieving higher success rates in complex long-horizon tasks.

Overview of "Policy Adaptation via Language Optimization: Decomposing Tasks for Few-Shot Imitation"

This paper presents a novel approach, termed Policy Adaptation via Language Optimization (PALO), designed to enhance the adaptability of language-conditioned robot policies in few-shot learning scenarios. The primary challenge addressed is the difficulty of pre-trained robotic policies to generalize to out-of-distribution tasks with limited demonstrations, particularly in complex, long-horizon manipulation tasks.

Methodology

The core of the method involves leveraging Vision-LLMs (VLMs) to decompose high-level task instructions into manageable subtasks. These subtasks guide a pre-trained language-conditioned robotic policy, enabling it to adapt quickly without extensive fine-tuning. The paper introduces PALO, which comprises several steps:

1. Decomposition of Tasks: Given a new task and a handful of examples, VLMs propose potential decompositions of the task into a sequence of subtasks. These are candidates for optimizing task execution.
2. Nonparametric Adaptation: The adaptation process uses a limited set of demonstrations as a calibration set to tune task decompositions. This contrasts with traditional approaches that fine-tune policy parameters directly, often requiring extensive data.
3. Optimization: The PALO algorithm optimizes the decompositions by selecting those that minimize the validation error on the calibration set. This process enhances the policy's performance on new tasks by focusing on cherry-picking optimal language sequences that align with observed behaviors.
4. Parallelism: The approach utilizes sampling of subtask partitions to improve efficiency, allowing the algorithm to evaluate multiple decompositions in a batched manner.

Results

Empirical results presented in the paper show that PALO significantly outperforms existing few-shot learning and pre-trained policy methods, such as Octo and RT-2-X, by achieving a higher success rate in long-horizon tasks with as few as five demonstrations. This indicates PALO's effectiveness in leveraging the semantic information provided by language to adapt to unseen tasks.

Implications and Future Work

The implications of this research are both practical and theoretical. Practically, PALO offers a robust method for deploying robotic systems in dynamic environments where pre-defined training datasets cannot encompass all potential tasks. Theoretically, it enriches understanding of how semantic information can bridge high-level task planning and low-level robotic control.

Future work could address the scalability of the sampling-based optimization process, particularly as task complexity grows. Additionally, exploring the use of advanced VLMs could enhance the accuracy of task decompositions, potentially integrating real-time feedback for adaptive learning.

In conclusion, the proposed PALO method introduces a promising direction in leveraging LLMs for task decomposition in robotic control, offering a compelling framework for few-shot learning in complex environments. This approach can pave the way for more adaptive robotic systems capable of performing a broader range of tasks with minimal supervision.