Make Your VLA More Robust Without More Data By Interleaving Motion Planning

• URL: http://arxiv.org/abs/2606.00985v1
• Date: Sun, 31 May 2026 03:52:53 GMT
• Title: Make Your VLA More Robust Without More Data By Interleaving Motion Planning
• Abstract summary: We present a framework that integrates model-based motion planning with VisionLanguageAction (VLA) models to improve robustness without further training.<n>We evaluate our approach on the BEHAVIOR-1K benchmark and demonstrate 113% improvement in task progress over a top end-to-end VLA baseline.
• Score: 3.8626902908968863
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language-Action (VLA) models have shown remarkable progress for mobile manipulation, but their performance on long-horizon tasks remains poor. These tasks are especially challenging because (1) progress toward high-level goals must be maintained across extended sequences of spatially distributed subtasks, and (2) early execution errors compound rapidly over the task horizon. These challenges persist despite finetuning on large human teleoperated mobile manipulation data, indicating that more data alone may not resolve the problem. To address these challenges, we propose MPVI: Motion Planner / VLA Interleaving, a framework that integrates model-based motion planning with VLAs to improve robustness without further training. The proposed integration enables localization and navigation to distant or occluded target objects through cluttered scenes using open-vocabulary object detection, frontier exploration and motion planning. However, such integration is non-trivial, requiring reliable switching between modules; we show one way forward via VLM-based completion checking with proprioceptive triggers. We evaluate our approach on the BEHAVIOR-1K benchmark and demonstrate 113% improvement in task progress over a top end-to-end VLA baseline. Additional details are available at the project page: https://mpvi.netlify.app/.

Related papers

• Beyond Data Scaling: Representation-Centric Continued Pre-training for Vision-Language-Action Models [120.24139942108405]
  Scaling robot data is crucial for building generalist Vision-Language-Action (VLA) models.<n>Under a fixed robot-data budget, continued pre-training must turn limited trajectories into transferable visual-action knowledge.<n>We propose VLAct, a VLA-oriented VLM backbone trained on broad, heterogeneous, multi-embodiment robot data.
  arXiv  Detail & Related papers  (2026-08-27T17:59:40Z)
• Long-Horizon Manipulation via Trace-Conditioned VLA Planning [55.80061850746898]
  LoHo-Manip is a framework that scales short-horizon VLA execution to long-horizon instruction following via a dedicated task-management VLM.<n>LoHo-Manip predicts a progress-aware remaining plan that combines a subtask sequence with an explicit done + remaining split as lightweight language memory.<n>The executor VLA is adapted to condition on the rendered trace, thereby turning long-horizon decision-making into repeated local control.
  arXiv  Detail & Related papers  (2026-04-23T17:59:04Z)
• AtomVLA: Scalable Post-Training for Robotic Manipulation via Predictive Latent World Models [9.608633915316252]
  Vision-Language-Action (VLA) models demonstrate remarkable potential for generalizable robotic manipulation.<n>Current paradigms rely on coarse, high-level task instructions during supervised fine-tuning.<n>We propose method, the first subtask-aware VLA framework integrated with a scalable offline post-training pipeline.
  arXiv  Detail & Related papers  (2026-03-09T15:52:48Z)
• LiLo-VLA: Compositional Long-Horizon Manipulation via Linked Object-Centric Policies [54.150202739999806]
  LiLo-VLA is a modular framework capable of zero-shot modularity to novel long-horizon tasks without ever being trained on them.<n>We introduce a 21-task simulation benchmark consisting of two challenging suites: LIBERO-Long++ and Ultra-Long.<n>In these simulations, LiLo-VLA achieves a 69% average success rate, outperforming Pi0.5 by 41% and OpenVLA-OFT by 67%.
  arXiv  Detail & Related papers  (2026-02-25T03:33:39Z)
• RoboInter: A Holistic Intermediate Representation Suite Towards Robotic Manipulation [104.68774434699158]
  RoboInter Manipulation Suite is a unified resource including data, benchmarks, and models of intermediate representations for manipulation.<n>It comprises RoboInter-Tool, a lightweight GUI that enables semi-automatic annotation of diverse representations, and RoboInter-Data, a large-scale dataset containing over 230k episodes across 571 diverse scenes.<n>RoboInter-VLA offers an integrated plan-then-execute framework, supporting modular and end-to-end VLA variants.
  arXiv  Detail & Related papers  (2026-02-10T17:01:54Z)
• From Knowing to Doing Precisely: A General Self-Correction and Termination Framework for VLA models [42.553888395227766]
  We propose a lightweight, training-free framework for vision-language-action (VLA) models.<n>VLA-SCT operates as a self-correcting control loop, combining data-driven action refinement with conditional logic for termination.<n>Our method achieves consistent improvements across all datasets in the LIBERO benchmark.
  arXiv  Detail & Related papers  (2026-02-02T08:44:40Z)
• V-CAGE: Context-Aware Generation and Verification for Scalable Long-Horizon Embodied Tasks [6.820118518027692]
  V-CAGE is a closed-loop framework for generating semantically aligned manipulation datasets at scale.<n>We propose a context-aware instantiation mechanism that enforces geometric consistency during scene synthesis.<n>We also employ a hierarchical instruction decomposition module to bridge the gap between abstract intent and low-level control.
  arXiv  Detail & Related papers  (2026-01-21T16:41:51Z)
• PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation [27.791908160098625]
  PALM builds policy learning around interaction-centric affordance reasoning and subtask progress cues.<n>Palm consistently outperforms baselines in simulation and real-world experiments.
  arXiv  Detail & Related papers  (2026-01-11T21:00:58Z)
• TrackVLA++: Unleashing Reasoning and Memory Capabilities in VLA Models for Embodied Visual Tracking [30.955088934475928]
  We present TrackVLA++, a novel model that enhances embodied visual tracking with two key modules, a spatial reasoning mechanism and atemporal Identification Memory (TIM)<n>TrackVLA++ achieves state-of-the-art performance on public benchmarks across both egocentric and multi-camera settings.
  arXiv  Detail & Related papers  (2025-10-08T15:29:17Z)
• Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting [70.83781268763215]
  Vision-language models (VLMs) have achieved impressive performance across diverse multimodal tasks by leveraging large-scale pre-training.<n>VLMs face unique challenges such as cross-modal feature drift, parameter interference due to shared architectures, and zero-shot capability erosion.<n>This survey aims to serve as a comprehensive and diagnostic reference for researchers developing lifelong vision-language systems.
  arXiv  Detail & Related papers  (2025-08-06T09:03:10Z)
• SP-VLA: A Joint Model Scheduling and Token Pruning Approach for VLA Model Acceleration [70.72227437717467]
  Vision-Language-Action (VLA) models have attracted increasing attention for their strong control capabilities.<n>Their high computational cost and low execution frequency hinder their suitability for real-time tasks such as robotic manipulation and autonomous navigation.<n>We propose SP-VLA, a unified framework that accelerates VLA models by jointly scheduling models and pruning tokens.
  arXiv  Detail & Related papers  (2025-06-15T05:04:17Z)
• Towards Unified Token Learning for Vision-Language Tracking [65.96561538356315]
  We present a vision-language (VL) tracking pipeline, termed textbfMMTrack, which casts VL tracking as a token generation task. Our proposed framework serializes language description and bounding box into a sequence of discrete tokens. In this new design paradigm, all token queries are required to perceive the desired target and directly predict spatial coordinates of the target.
  arXiv  Detail & Related papers  (2023-08-27T13:17:34Z)

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.