ROBOTNESS
Research

Papers

New robotics and physical-AI papers, with what each one means for the industry.

3 papers
TopicAllManipulation · 16VLA · 15Navigation · 11Perception · 9Robotics · 9Learning · 6Humanoid · 5Safety · 3Simulation · 3Adaptive action horizons · 1Aerial Manipulation · 1Co-speech Humanoid · 1Continuum sensing · 1Distributed Manipulation · 1Floorplan SLAM · 1Grounding · 1Humanoid control · 1Locomotion · 1Memory-centric VLA · 1Model-based RL · 1Multi-UAV State Estimation · 1Policy · 1Real-time VLA · 1Safe RL-MPC · 1Safe VLA · 1Soft Pneumatic Actuators · 1Tactile · 1Terrain traversability · 1Text-to-3D Policy · 1Tool Co-Design · 1Trajectory Correction · 1UAV Navigation · 1UAV traffic monitoring · 1Underwater active mapping · 1Vision-Language Navigation · 1Visual Grounding · 1World models · 1World-action model · 1
LevelAnyBeginnerIntermediateExpert
Filtered by technology vla · clear
arXiv
Memory-centric VLAIntermediate

Inline Memory Meets Reusable Skills: Memory-centric Framework for Vision-Language-Action Model

Zaijing Li, Rui Shao, Bing Hu, Haoyu Zhang, Dongmei Jiang, Liqiang Nie

This preprint introduces Optimus-R, a memory-centric VLA framework that turns robotic manipulation adaptation into explicit query-skill memory tuning instead of repeated parameter updates. With only 30% of training data it raises LIBERO average success to 88.6% (vs 72.1% for π0.5) and reaches 33.3% real-world success from 20 demos per task; in lifelong learning it reduces forgetting to 10.0% vs 15.0%. The approach matters for low-data robot adaptation and retaining prior skills.

VLA
Read the full paper (PDF)
arXiv
VLAIntermediate

ChunkTrust: Adapting Execution Horizons for Robot Policies with Action-Expert Evidence

Fanding Huang, Jingyan Jiang, Shifeng Bao, Mingkang Pu, Shiwei Li, Jing Xu, Shijia Xu, Guanbo Huang, Chenghao Gu, Yuzhi Huang, Chenxin Li, Faisal Nadeem Khan, Huan Yang, Yan Wang, Cheng Chi, Zhi WangTsinghua University, Beijing Academy of Artificial Intelligence (BAAI), Renmin University of China, Shenzhen Technology University, Hefei University of Technology, Jiangnan University, Chongqing University, The Chinese University of Hong Kong

Robot AI models plan a short burst of movements at a time, and how many of those moves the robot carries out before it looks again is usually fixed by hand. This preprint from Tsinghua University, BAAI and partners adds a plug-in that picks that number while the robot works, raising success rates of existing Physical Intelligence and NVIDIA models in simulation and on a real two-arm robot without retraining them.

VLARobot Foundation ModelsImitation Learning
Physical IntelligenceNVIDIA
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Topic pages

Manipulation · 16VLA · 14Navigation · 11Perception · 9General robotics · 9Robot learning · 6Humanoid · 5Safety · 3Simulation · 3

Papers come from arXiv robotics feeds. Where our summary is not written yet, you see the opening lines of the abstract.