Các bài nghiên cứu về robot — Trang 18

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 thg 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 thg 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 thg 7

Scaling Behavior Foundation Model Cuts Humanoid Control Errors by 82%

Weishuai Zeng, Kangning Yin, Xiaojie Niu +15

Robotics18 thg 7

AHEAD Teleoperation Predicts Human Intent to Slash Robot Reaction Time

Seok Joon Kim, Junho Lee, Federica Spinola +2

Robotics17 thg 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 thg 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 thg 7

Context Scaling Unlocks Generalist Robot Policies That Learn on the Fly

Yunfan Jiang, Yevgen Chebotar, Ruijie Zheng +8

drones16 thg 7

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

Zhiyun Deng, Luis Sentis

Industrial16 thg 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 thg 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 thg 7

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

Yixiang Chen, Peiyan Li, Yuan Xu +13

AI15 thg 7

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

Zhouchonghao Wu, Akshay Rangesh, Weixin Li +4

Robotics15 thg 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 thg 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 thg 7

A Minimalist Recipe for Dexterous Manipulation Using Human Demonstration Retargeting

Yunhai Feng, Natalie Leung, Jiaxuan Wang +3

Robotics14 thg 7

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

Dian Wang, Jisang Park, Xiaomeng Xu +3

Robotics13 thg 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 thg 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