RationalVLA: A Rational Vision-Language-Action Model with Dual System

TL;DR

RationalVLA is a dual-system model that improves robotic manipulation by understanding, reasoning, and rejecting infeasible natural language instructions, demonstrated on a new challenging benchmark with diverse defective commands.

Contribution

The paper introduces RAMA, a new benchmark with over 14,000 samples of defective instructions, and proposes RationalVLA, a dual vision-language-action model that effectively handles ambiguous and infeasible commands.

Findings

RationalVLA achieves 14.5% higher success rate on RAMA.

It effectively rejects infeasible instructions.

It maintains competitive performance on standard tasks.

Abstract

A fundamental requirement for real-world robotic deployment is the ability to understand and respond to natural language instructions. Existing language-conditioned manipulation tasks typically assume that instructions are perfectly aligned with the environment. This assumption limits robustness and generalization in realistic scenarios where instructions may be ambiguous, irrelevant, or infeasible. To address this problem, we introduce RAtional MAnipulation (RAMA), a new benchmark that challenges models with both unseen executable instructions and defective ones that should be rejected. In RAMA, we construct a dataset with over 14,000 samples, including diverse defective instructions spanning six dimensions: visual, physical, semantic, motion, safety, and out-of-context. We further propose the Rational Vision-Language-Action model (RationalVLA). It is a dual system for robotic arms that integrates the high-level vision-language model with the low-level manipulation policy by introducing learnable latent space embeddings. This design enables RationalVLA to reason over instructions, reject infeasible commands, and execute manipulation effectively. Experiments demonstrate that RationalVLA outperforms state-of-the-art baselines on RAMA by a 14.5% higher success rate and 0.94 average task length, while maintaining competitive performance on standard manipulation tasks. Real-world trials further validate its effectiveness and robustness in practical applications. Our project page is https://irpn-eai.github.io/RationalVLA.