E-TTS: A New Embodied Test-Time Scaling Framework for Robotic Manipulation

• URL: http://arxiv.org/abs/2606.27268v1
• Date: Thu, 25 Jun 2026 16:50:21 GMT
• Title: E-TTS: A New Embodied Test-Time Scaling Framework for Robotic Manipulation
• Abstract summary: Embodied Test-Time Scaling is a framework that unifies reasoning and action scaling for robotic manipulation.<n>E-TTS uses a history buffer to store historical context, which is then used by reasoning and action verifiers to evaluate the sampled candidates.<n>Results: E-TTS consistently improves performance, achieving up to a 33.14% increase in simulation and 26.62% in real-world scenarios.
• Score: 21.23579664383006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, a few works have made early attempts to study test-time scaling for embodied tasks. However, two major challenges remain unsolved: (1) reasoning can effectively improve the performance of the policy, but its scaling mechanism has seldom been studied; (2) historical information is essential, as embodied tasks are inherently long-horizon and sequential, making sole reliance on current observations for action scaling inadequate due to the lack of historical context utilization. To address these challenges, we introduce E-TTS, a modular and plug-and-play Embodied Test-Time Scaling framework that unifies reasoning and action scaling for robotic manipulation via history-aware iterative refinement with vision-language verifiers. To support joint reasoning-action scaling, E-TTS performs reasoning-action joint sampling and scoring in a pairwise manner. To better utilize historical information, E-TTS uses a history buffer to store historical context, which is then used by reasoning and action verifiers to evaluate the sampled candidates. Unlike conventional open-loop TTS methods, E-TTS introduces feedback generation into the sampling process to form a closed-loop iterative refinement mechanism, enhancing both inference efficiency and environmental adaptability. Each component functions as an independent and composable module, allowing flexible and adaptive configuration depending on task requirements. To evaluate the advantages of our framework, we conduct experiments across 4 different benchmarks, 6 environments, 3 embodiments, and 4 base vision-language-action models. The experimental results demonstrate that, without requiring additional expert data collection or retraining, E-TTS consistently improves performance, achieving up to a 33.14% increase in simulation and 26.62% in real-world scenarios.

Related papers

• Dual-level Adaptation for Multi-Object Tracking: Building Test-Time Calibration from Experience and Intuition [29.371401864045115]
  Test-Time Adaptation (TTA) has emerged as a promising paradigm to alleviate such distribution shifts.<n>This paper propose a Test-time from Experience and Intuition (TCEI) framework.<n>In this framework, the system utilizes transient memory to recall recently observed objects for rapid predictions.<n>Both confident and uncertain objects during online testing are exploited as historical priors and reflective cases.
  arXiv  Detail & Related papers  (2026-03-23T06:50:28Z)
• UniT: Unified Multimodal Chain-of-Thought Test-time Scaling [85.590774707406]
  Unified models can handle both multimodal understanding and generation within a single architecture, yet they typically operate in a single pass without iteratively refining their outputs.<n>We introduce UniT, a framework for multimodal test-time scaling that enables a single unified model to reason, verify, and refine across multiple rounds.
  arXiv  Detail & Related papers  (2026-02-12T18:59:49Z)
• Specialization after Generalization: Towards Understanding Test-Time Training in Foundation Models [64.02612380298228]
  Recent studies have explored the idea of continuing to train a model at test-time for a given task, known as test-time training (TTT)<n>We propose a model in which TTT achieves a substantially smaller in-distribution test error than global training.<n>We empirically validate our model's key assumptions by training a sparse autoencoder on ImageNet.
  arXiv  Detail & Related papers  (2025-09-29T09:24:52Z)
• Sticker-TTS: Learn to Utilize Historical Experience with a Sticker-driven Test-Time Scaling Framework [97.11629413081651]
  We propose Sticker-TTS, a novel test-time scaling framework for large reasoning models.<n>At the core of our framework are distilled key conditions-termed stickers that drive the extraction, refinement, and reuse of critical information.<n>We show that Sticker-TTS consistently surpasses strong baselines, including self-consistency and advanced reinforcement learning approaches.
  arXiv  Detail & Related papers  (2025-09-05T11:14:11Z)
• Test-Time Alignment for Tracking User Interest Shifts in Sequential Recommendation [47.827361176767944]
  Sequential recommendation is essential in modern recommender systems, aiming to predict the next item a user may interact with.<n>Real-world scenarios are often dynamic and subject to shifts in user interests.<n>Recent Test-Time Training has emerged as a promising paradigm, enabling pre-trained models to dynamically adapt to test data.<n>We propose T$2$ARec, a novel model leveraging state space model for TTT by introducing two Test-Time Alignment modules tailored for sequential recommendation.
  arXiv  Detail & Related papers  (2025-04-02T08:42:30Z)
• Learning to Reason from Feedback at Test-Time [19.576076567451434]
  Iterative interaction with the environment and feedback is often required to achieve success.<n>Existing approaches either struggle with length generalization or rely on naive retries without leveraging prior information.<n>We introduce FTTT, a novel paradigm that formulates feedback utilization as an optimization problem at test time.
  arXiv  Detail & Related papers  (2025-02-16T11:05:27Z)
• A Systematic Examination of Preference Learning through the Lens of Instruction-Following [83.71180850955679]
  We use a novel synthetic data generation pipeline to generate 48,000 instruction unique-following prompts.<n>With our synthetic prompts, we use two preference dataset curation methods - rejection sampling (RS) and Monte Carlo Tree Search (MCTS)<n>Experiments reveal that shared prefixes in preference pairs, as generated by MCTS, provide marginal but consistent improvements.<n>High-contrast preference pairs generally outperform low-contrast pairs; however, combining both often yields the best performance.
  arXiv  Detail & Related papers  (2024-12-18T15:38:39Z)
• Improved Test-Time Adaptation for Domain Generalization [48.239665441875374]
  Test-time training (TTT) adapts the learned model with test data. This work addresses two main factors: selecting an appropriate auxiliary TTT task for updating and identifying reliable parameters to update during the test phase. We introduce additional adaptive parameters for the trained model, and we suggest only updating the adaptive parameters during the test phase.
  arXiv  Detail & Related papers  (2023-04-10T10:12:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.