HABERLER

Tüm Haberler

Yapay zekâ dünyasından son haberler, derinlikli analizler, teknik incelemeler.

115Yazı

1Yazar

5.2KOkuma · 30g

5Bu Hafta

SIRALA

EN YENİ EN ÇOK OKUNAN TARTIŞILAN EDİTÖR SEÇİMİ

FORMAT

Tümü 115 Haber Analiz Rehber Görüş

arXiv

FERPO: Forward Entropy-Regularized Policy Optimization

Sebastian Sanokowski, Alireza Sarmadi, Majid Khadiv

Several state-of-the-art methods for online reinforcement learning in continuous control improve pol...

arXiv

InterEvolve: Test-Time Evolution of Reward Programs for Humanoid Loco-Manipulation

Zhuo Lin, Sirui Xu, Liuyu Bian et al.

We study test-time evolution for humanoid loco-manipulation: solving tasks that a controller was nev...

arXiv

AssemblyWorld: Rethinking 3D Assembly with General-Purpose Agents

Jiahao Zhang, Yeying Fan, Moitreya Chatterjee et al.

The task of 3D assembly requires translating an understanding of parts and their relationships into ...

arXiv

Ego4WAM: What Matters When Scaling Egocentric Human Data for Robot Learning?

Zhihao Sun, Liu Liu, Xinjiang Wang et al.

Egocentric human data provides a scalable source of experience for robot learning, but varies substa...

arXiv

DynaHarness: A Dynamic Physical Harness for Self-Evolving Robot Agents

Haoyuan Deng, Jiebin Liu, Tengxiao Zhang et al.

Pretrained robot policies provide useful action priors, but long-horizon manipulation still requires...

arXiv

Skill-Space Shooting for Autonomous Robot Policy Improvement

Zihang Rui, Renhao Wang, Haoxu Huang et al.

Robots deployed in the physical world must be able to improve beyond their initial training as they ...

arXiv

Counterfactual Video Generation Enables Scalable Humanoid Loco-Manipulation

Zihan Wang, Zhen Wu, Pieter Abbeel et al.

Teaching humanoids loco-manipulation skills, such as carrying diverse objects, via visual imitation ...

arXiv

Rethinking Representations for World-Action Modeling

Haoyi Jiang, Liu Liu, Xinjiang Wang et al.

World-action models jointly learn robot policies and predict future observations, making the represe...

arXiv

Statistical Learning of Contractive Dynamical Representations for Composite Adaptive Control

Min Kim, José Leonardo Brenes, Fred Hadaegh et al.

We present a representation-learning framework for composite adaptive tracking control under dynamic...

arXiv

Systematic Multi-Agent Vision-and-Language Navigation: Formulation, Benchmark, and Method

Yunzhe Xu, Zhe Liu

Vision-and-Language Navigation (VLN) has largely focused on a single agent following a single instru...

arXiv

ENCP: Episode-Normalized Conformal Prediction for Vision-and-Language Navigation

Vicky Feliren, A. Taufiq Asyhari, Muhamad Risqi U. Saputra

Uncertainty estimation for Vision-Language-Navigation (VLN) models is a critical task since it can h...

arXiv

Çoklu Görev Manipülasyonu için Kendi Kendini Gösteren Üretken Kontrole Sahip VLA’ların İnce Ayarı

Prachi Garg, Steve Xing, Prahit Yaugand et al.

Pi0.5 gibi son teknoloji ürünü görüş-dil-eylem (VLA) modelleri, güçlü anlamsal anlayış, talimat taki...

arXiv

DriftWorld: Fast World Modeling through Drifting

Susie Lu, Haonan Chen, Weirui Ye et al.

Predictive world models enable robots to simulate the visual outcomes of their actions, but state-of...

arXiv

An offline approach to fNIRS-guided reinforcement learning for robot behavior

Julia Santaniello, Madelaine Brower, Benson Jiang et al.

Human-in-the-loop Reinforcement Learning has become a popular approach for training, finetuning, and...

arXiv

GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks

Kaiyuan Chen, Shuangyu Xie, Letian Fu et al.

For robots to work reliably in commercial and industrial applications, recent advances in agentic co...