Hugging Face Trending Papers
SharedKV-BT: Node-Local Typed Decisions for Behavior-Tree Agents
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arXiv Computation and Language
Oct 7
SharedKV-BT: Node-Local Typed Decisions for Behavior-Tree Agents
arXiv:2610.07327v1 Announce Type: cross Abstract: Agent tasks require sequences of interdependent decisions. Autoregressive models support more flexible decision interfaces than conventional classifi...
arXiv AI
Sep 25
Design and Evaluation of LLM Chaining-Based Task Planning for General Purpose Service Robots
The paper introduces an LLM chaining architecture for General Purpose Service Robots that splits instruction classification and action generation into two stages, cutting prompt length by about 45% and boosting planning consistency. Evaluation on 100 synthetic GPSR commands across three language models shows consistent improvements over single-prompt methods, with up to +37 percentage points gain on local models. Real‑robot trials on the Toyota HSR confirm that while planning success improves, execution-layer failures remain the main obstacle to full task completion.
arXiv AI
Jun 9
QuickLAP: Quick Language-Action Preference Learning for Semi-Autonomous Agents
arXiv:2511. 17855v5 Announce Type: replace Abstract: Robots must learn from both what people do and what they say, but either modality alone is often incomplete: physical corrections are grounded but ambiguous in intent, while language expresses high-level goals but lacks physical grounding.
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
Aug 28
STEP: State-Aware Task Estimation and Planning with Multi-Modal LLMs for Human-Robot Collaboration
The paper introduces STEP, a State‑Aware Task Estimator and Planner that uses multi‑modal large language models to explicitly estimate system states and predict state transitions during task planning. By forecasting future states alongside actions, STEP reduces hallucinated actions and improves task‑convergent planning. In a simulated robot assembly task, STEP outperforms the state‑of‑the‑art by 32.8% in action executability and 14.8% in final‑state error.
arXiv AI
Sep 25
Jev-Mobile: Jev as an Executor for Mobile GUI Agents
Jev-Mobile introduces a new approach for mobile GUI agents by separating high‑frequency lightweight execution from low‑frequency vision‑language model (VLM) planning. The VLM sets local goals, the accessibility tree provides a structured action space, and Jev, a fast typed decision model, repeatedly selects actions within this space, allowing multiple GUI actions per VLM decision. On the AndroidWorld task suite, Jev-Mobile achieves 79% task success, reduces mean end‑to‑end execution time by 32.7%, and cuts model API cost by 73.4% compared to a step‑wise VLM baseline.