arXiv AI
Jul 14
VIA: Visual Interface Agent for Robot Control
arXiv:2607. 11119v1 Announce Type: cross Abstract: Robot manipulation is a complex task that requires visual understanding, physical reasoning, planning, and closed-loop control.
ACE: Agentic Control for Embodied Manipulation via Zero-shot Workflow Reasoning
arXiv:2607. 04162v1 Announce Type: cross Abstract: Open-ended tabletop manipulation requires agents to not only understand natural language but also adapt to dynamic environments and execution failures.
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arXiv AI
Jul 7
Cortex: A Bidirectionally Aligned Embodied Agent Framework for Long-horizon Manipulation
arXiv:2607. 05377v1 Announce Type: cross Abstract: While recent Vision-Language-Action (VLA) models show promise toward generalist manipulation policies, they struggle with long-horizon tasks due to their Markovian nature-relying solely on current observations.
arXiv AI
Jul 28
Unified Embodied VLM Reasoning with Robotic Action via Autoregressive Discretized Pre-training
arXiv:2512. 24125v3 Announce Type: replace-cross Abstract: General-purpose robotic systems operating in open-world environments must achieve both broad generalization and high-precision action execution, a combination that remains challenging for existing Vision-Language-Action (VLA) models.
arXiv AI
4d ago
Explore, Execute, Evolve: A Skill Acquisition and Reuse Loop for Embodied Agents
arXiv:2609.37810v1 Announce Type: cross Abstract: Vision-language-action and world-action models have demonstrated impressive capabilities in robotics, yet generalization to unseen tasks remains chal...
Hugging Face Trending Papers
4d ago
Explore, Execute, Evolve: A Skill Acquisition and Reuse Loop for Embodied Agents
Vision-language-action and world-action models have demonstrated impressive capabilities in robotics, yet generalization to unseen tasks remains challenging. More recently, general-purpose multimodal...
arXiv AI
4d ago
Guava: Distilling Frontier VLMs into a Compact Agent through a Robotic Manipulation Harness
arXiv:2606.18363v3 Announce Type: replace-cross Abstract: Language models trained on large-scale vision-language data have demonstrated strong potential for embodied agents. Harnessing models through...
arXiv AI
Aug 17
Evolve Vision-Language-Action Model into an Agent with On-the-fly Tool-use
arXiv:2608. 14047v1 Announce Type: cross Abstract: This paper integrates end-to-end Visual-Language-Action (VLA) models with agentic tool-use to propose Agentic Robot with Tool-use (ART).
arXiv AI
Jul 28
A Few Words Go a Long Way: Language Guided Robot Policy Synthesis
arXiv:2607. 23784v1 Announce Type: cross Abstract: While vision-language-action models have demonstrated impressive zero-shot manipulation capabilities, they remain fundamentally black box policies that are difficult to interpret, adapt, or correct when they inevitably fail.
arXiv AI
Sep 7
RoboSPA: Can VLA Models Go Beyond Simple Scenes and Short-Horizon Tasks?
RoboSPA is a large-scale robotic manipulation dataset and benchmark designed to evaluate Vision‑Language‑Action models on fine‑grained spatial reasoning and long‑horizon procedural planning. It contains 10 task categories, 56 base tasks, and 280 variants across five difficulty levels, with 527K trajectories collected from multiple embodiments and scenes. The benchmark introduces diagnostic metrics beyond binary success, revealing that current VLA models struggle with complex spatial relations, precise execution, and memory‑intensive planning.
arXiv AI
Jun 30
Demonstration-Free Robotic Control via LLM Agents
arXiv:2601. 20334v2 Announce Type: replace-cross Abstract: Robotic manipulation has increasingly adopted vision-language-action (VLA) models, which achieve strong performance but typically require task-specific demonstrations and fine-tuning, and often generalize poorly under domain shift.
arXiv Computer Vision
2d ago
Guide, Think, Act: Interactive Embodied Reasoning in Vision-Language-Action Models
The paper introduces GTA‑VLA, an interactive Vision‑Language‑Action framework that lets users guide robot policies with explicit visual cues such as affordance points, boxes, and traces. Unlike traditional direct sense‑to‑act models, GTA‑VLA incorporates a spatial‑visual Chain‑of‑Thought that blends human guidance with internal task planning, and couples this reasoning module with a lightweight reactive action head for efficient execution. Experiments on the SimplerEnv WidowX benchmark show a state‑of‑the‑art 81.2 % success rate, and the framework significantly improves task success under out‑of‑domain visual shifts and spatial ambiguities, demonstrating the benefit of interactive reasoning for failure recovery in embodied control.
arXiv AI
Aug 11
REMAC: Self-Reflective and Self-Evolving Multi-Agent Collaboration for Long-Horizon Robot Manipulation
arXiv:2503. 22122v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have demonstrated remarkable capabilities in robotic planning, particularly for long-horizon tasks that require a holistic understanding of the environment for task decomposition.