Physical AI, robots, and autonomous systems
![[2603.13846] Is Seeing Believing? Evaluating Human Sensitivity to Synthetic Video](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
Abstract page for arXiv paper 2603.13846: Is Seeing Believing? Evaluating Human Sensitivity to Synthetic Video
Abstract page for arXiv paper 2603.09455: Declarative Scenario-based Testing with RoadLogic
Abstract page for arXiv paper 2601.20404: On the Impact of AGENTS.md Files on the Efficiency of AI Coding Agents
The paper presents Robo-Saber, a motion generation system designed for playtesting virtual reality games, specifically focusing on genera...
This paper presents a framework for autonomous vehicles to safely interact with cyclists by integrating Hamilton-Jacobi reachability anal...
This article presents a novel co-design framework for optimizing end-effectors in robotics, specifically for manipulating deformable and ...
This paper introduces Dual-Channel Attention Guidance (DCAG), a novel training-free method for enhancing image editing control in Diffusi...
The paper introduces CLUTCH, a novel model for generating hand motions from text, leveraging a new dataset and advanced techniques to imp...
This paper presents a novel nested training approach for enhancing mutual adaptation in human-AI teaming, addressing challenges in agent ...
The paper presents ROCKET, a novel framework for enhancing Vision-Language-Action models by employing residual-oriented multi-layer align...
This paper explores the concept of noise-agnostic generative models, specifically diffusion models, and argues that they do not require e...
The article presents a novel approach to locomotion control in fruit flies using a whole-brain connectomic graph model, demonstrating enh...
This article presents a novel approach to offline reinforcement learning by integrating cross-embodiment learning to enhance robot policy...
This paper presents a novel approach to learning optimal and sample-efficient decision policies in reinforcement learning, addressing cha...
This paper presents APEMO, a novel runtime scheduling layer designed to enhance the reliability of long-horizon agentic systems by optimi...
MePoly introduces a novel polynomial energy-based model for policy optimization in stochastic control, enhancing multi-modality represent...
This paper explores the impact of target class selection on the compressibility of neural networks for avian monitoring using energy-auto...
This paper presents a novel approach to multi-target active debris removal in Low Earth Orbit using deep reinforcement learning, co-ellip...
This paper discusses reducing text bias in synthetically generated multiple-choice question answering (MCQA) for Vision Language Models (...
Krafton names Kangwook Lee CAIO, launches Ludo Robotics in Korea Kangwook Lee tapped to steer AI strategy as Krafton spins off Ludo Robot...
The article discusses the engineering of a deterministic kill-switch for autonomous agents, emphasizing the importance of safety mechanis...
The article discusses the evolving role of AI in businesses, highlighting the emergence of autonomous systems that perform specific tasks...
The article discusses a defense company's development of AI agents designed for military applications, raising ethical concerns about aut...
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