Robotiikan tutkimusartikkelit — sivu 18

Robotics19.7.

Semantic Communication Cuts Vehicle Data Needs at Intersections

Goal-oriented semantic communication reduces data transmission by 80%+ in vehicle intersection coordination without sacrificing throughput.

Wenjie Liu, Yansha Deng

Robotics19.7.

Robot Juggles Balls with Human Partners in Real Time

Researchers built a real-time planning and control system that enables a robot arm to catch and throw balls with a human partner, sustaining one-, two-, and …

Jonathan Rainer Lippert, Kai Ploeger, Abir Chowdhury +3

Robotics18.7.

Social Robots That Assess Frailty and Fall Risk in Older Adults: A Real-World Clinical Study

Frailty is a key predictor of adverse health outcomes in older adults, and early detection through standardised assessments such as the Short Physical Perfor…

Aniol Civit, Antonio Andriella, Alba Martínez +4

Humanoid18.7.

Scaling Behavior Foundation Model Cuts Humanoid Control Errors by 82%

Weishuai Zeng, Kangning Yin, Xiaojie Niu +15

Robotics18.7.

AHEAD Teleoperation Predicts Human Intent to Slash Robot Reaction Time

Seok Joon Kim, Junho Lee, Federica Spinola +2

Robotics17.7.

Stigmergic Graph Memory Boosts Warehouse Robot Throughput by 20-36%

A new memory layer called Stigmergic Graph Memory (SGM) helps fleets of warehouse robots avoid traffic jams by learning from recent execution patterns. In be…

Aditya Dutta, Joon-Seok Kim

AI17.7.

When World-Action Models Dream Right But Act Wrong: A Study of Action-Imagination Desynchronization

BadWAM attacks world-action models by desynchronizing imagined futures from executed actions, causing task failure even when predictions appear normal.

Qi Li, Xingyi Yang, Xinchao Wang

AI17.7.

Context Scaling Unlocks Generalist Robot Policies That Learn on the Fly

Yunfan Jiang, Yevgen Chebotar, Ruijie Zheng +8

drones16.7.

AeroMap3D Anchors Drones to Maps for 6-DoF Localization Without GPS

Zhiyun Deng, Luis Sentis

Industrial16.7.

Industrial Dexterity Benchmark: Hardware-Software Platform for Industrial Robot Manipulation

A hardware-software benchmarking platform for industrial dexterous manipulation is presented, comprising three custom-designed IDB boards.

Honglu He, Jacob Laufer, Zhiwu Zheng +8

Robotics16.7.

PhysClaw-0: A Robot That Learns From Natural Language Corrections, Not Teleoperation

PhysClaw-0 lets operators correct robots with natural language during autonomous data collection, matching teleoperation data quality with less human time.

Boyuan Wang, Zhenyuan Zhang, Zhiqin Yang +16

Robotics15.7.

Optical Flow as a Unified Action Model: How FlowWAM Turns Pixel Motion Into Robot Commands

Yixiang Chen, Peiyan Li, Yuan Xu +13

AI15.7.

TerraZero: Zero-Demonstration Self-Play for Autonomous Driving at Scale

Zhouchonghao Wu, Akshay Rangesh, Weixin Li +4

Robotics15.7.

DenseReward: Teaching Robots Fine-Grained Task Progress with Dense Reward Signals from Failure Examples

We present DenseReward, a dense vision-language reward model for robotic manipulation that learns from failure synthesis.

Yu Fang, Wanxi Dong, Jiaqi Liu +7

Humanoid14.7.

How Retractable Cleats Help Bipedal Robots Walk on Slippery Slopes

Researchers have built a robotic foot with motor-driven retractable cleats that automatically adjust their depth based on terrain penetrability, enabling bip…

Deniz Kerimoglu, Junnosuke Kamohara, Jiyeon Maeng +4

AI14.7.

A Minimalist Recipe for Dexterous Manipulation Using Human Demonstration Retargeting

Yunhai Feng, Natalie Leung, Jiaxuan Wang +3

Robotics14.7.

Mixture of Frames: How Multiple Coordinate Frames Boost Bimanual Robot Manipulation

Dian Wang, Jisang Park, Xiaomeng Xu +3

Robotics13.7.

Diffusion Models Power Communication-Free Multi-Robot Teams

CoDiMAD uses diffusion models and privileged distillation to let robot teams coordinate without any communication, outperforming standard RL on coverage, pursuit, and box-pushing tasks.

Jiyue Tao, Shunheng Xin, Tongsheng Shen +2

AI13.7.

PAC-ACT Uses Reinforcement Learning to Sharpen Pretrained Robot Policies

Researchers from Wuhan University introduce PAC-ACT, a reinforcement learning (RL) post-training framework that fine-tunes pretrained Action Chunking Transfo…

Yujie Pang, Zudong Li