arXiv AI
Encore: Few-Shot Agentic Discovery of Manipulation Strategies
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arXiv AI
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World Action Agent: Harnessing VLMs for Robot Manipulation via World Action Rehearsal
World Action Agent (WAA) is a multi‑agent framework that lets vision‑language models (VLMs) directly pilot robots by operating within a visual action workspace. The workspace provides automatically selected contact views, editable action rehearsals, and in‑view correction to refine decisions before low‑level execution. WAA learns procedural skills from expert videos and human teaching, and its interaction traces can train smaller VLMs, achieving state‑of‑the‑art success on LIBERO‑Pro and improving out‑of‑domain performance on robosuite and Qwen3.5‑9B.
arXiv Computer Vision
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EVO-WAM: Evolving World Action Models through Video-Action Verification
arXiv:2609.38057v1 Announce Type: new Abstract: Improving robot policies on new tasks without collecting additional expert demonstrations remains a central challenge in robot learning. World action m...
arXiv AI
Sep 10
DISEIL: Demonstration Distillation for Sample-Efficient Imitation Learning
arXiv:2609.08123v1 Announce Type: cross Abstract: A robot that can be taught a new task from a handful of demonstrations has to work out for itself what it still cannot do, and then ask for exactly t...
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 AI
Jul 2
ASPIRE: Agentic /Skills Discovery for Robotics
arXiv:2607. 00272v1 Announce Type: cross Abstract: Traditional robot programming is challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse configurations and execution failures.
arXiv AI
Jun 26
WatchAct: A Benchmark for Behavior-Grounded Robot Manipulation
arXiv:2606. 26443v1 Announce Type: cross Abstract: A robot working alongside people must reason about what they have done, in what order, and with what intent.