Task Reconstruction and Extrapolation for $π_0$ using Text Latent (2505.03500v3)

Abstract: Vision-language-action models (VLAs) often achieve high performance on demonstrated tasks but struggle significantly when required to extrapolate, combining skills learned from different tasks in novel ways. For instance, VLAs might successfully put the cream cheese in the bowl and put the bowl on top of the cabinet, yet still fail to put the cream cheese on top of the cabinet. In this work, we demonstrate that behaviors from distinct tasks can be effectively recombined by manipulating the VLA's internal representations at inference time. Concretely, we identify the text latent by averaging the text tokens' hidden states across all demonstrated trajectories for a specific base task. For executing an extrapolated task, we can temporally interpolate the text latent of the two base tasks and add it back to the text hidden states, so sub-behaviors from the two tasks will be activated sequentially. We evaluate this approach using the newly created libero-ood benchmark, featuring 20 tasks extrapolated from standard LIBERO suites. The results on libero-ood show that all SOTA VLAs achieve < 15% success rate, while $\pi0$ with text latent interpolation reaches an 83% success rate. Further qualitative analysis reveals a tendency for VLAs to exhibit spatial overfitting, mapping object names to demonstrated locations rather than achieving genuine object and goal understanding. Additionally, we find that decoding the text latent yields human-unreadable prompts that can nevertheless instruct the VLA to achieve a 70% success rate on standard LIBERO suites, enabling private instruction or backdoor attacks.

Task Reconstruction and Extrapolation for $\pi_0$ using Text Latent

In the current landscape of Vision-Language-Action models (VLAs), the challenge of extrapolating learned skills to novel tasks remains significant. Despite the high performance VLAs demonstrate on specific tasks through direct fine-tuning and demonstrations, their ability to recombine learned behaviors from distinct tasks in novel contexts is considerably limited. The paper "Task Reconstruction and Extrapolation for $\pi_0$ using Text Latent" addresses this issue by proposing methods to manipulate models' internal representations during inference to enable behavior recombination for task extrapolation effectively.

Core Methodology

The approach introduced in the paper revolves around identifying and utilizing 'text latent' representations within VLAs. These latents are derived by averaging text tokens' hidden states across demonstrated trajectories of known tasks. For task extrapolation, text latent interpolation is proposed to temporally interpolate these representations of two base tasks, adding them back sequentially to the text hidden states during inference. This process activates sub-behaviors sequentially to accomplish new tasks that require skill combination.

Quantitative Analysis

Utilizing the 'libero-ood' benchmark, which comprises tasks extrapolated from existing ones, the paper evaluates various VLAs, including $\pi_0$, a state-of-the-art model. On novel tasks, existing VLAs achieve a less than 15% success rate. In stark contrast, $\pi_0$ augmented with text latent interpolation reaches an 83% success rate. This substantial numerical improvement underscores the efficacy of manipulating internal model states for recombining learned behaviors.

Additionally, qualitative analysis reveals that many VLAs suffer from spatial overfitting. Objects are associated with fixed locations observed in demonstrations, rather than understanding object identities or goals. This revelation has implications for how VLAs interpret and execute tasks, providing insights into their current limitations in spatial awareness and object recognition.

Implications and Future Directions

The implications of this research extend into both practical deployment and the theoretical understanding of VLAs. Practically, the ability to recombine existing skills to tackle new tasks can dramatically enhance the adaptability and utility of robotic systems in real-world applications. Theoretically, the manipulation of internal representations challenges the traditional model of learning as an immutable process, suggesting that flexible and dynamic model introspection can significantly advance AI capabilities.

Future directions might explore the universality and limitations of the proposed method across diverse VLA architectures and extend the scope of text latent manipulation to interrogate more complex task combinations and multi-modal environments. Also, the inherent spatial overfitting suggests a need for improved methodologies in training VLAs to learn object and spatial concepts more abstractly, enabling genuine understanding beyond specific demonstrated contexts.

In conclusion, "Task Reconstruction and Extrapolation for $\pi_0$ using Text Latent" presents compelling evidence that through strategic manipulation of text latent representations, VLAs can extend their capability beyond the rigid confinement of trained tasks, offering a promising avenue for enhancing artificial intelligence systems' adaptability and efficiency in dynamic environments.