ASPIRE is a continual learning system that autonomously writes and refines robot control programs in a code-as-policy paradigm, discovering transferable skills and surpassing prior methods on various…

This paper addresses the vulnerability of DNNs used in robotic semantic segmentation to adversarial attacks by proposing specialized detection strategies to enhance safety in robotic perception system…

GSAM introduces a generalizable and safe robotic framework for articulated object manipulation, significantly improving success rates and reducing variability across diverse tasks by integrating commo…

HydraCollab is a new adaptive collaborative-perception framework that selectively transmits informative sensor features and dynamically employs collaboration strategies to minimize communication overh…

This paper organizes embodied data sources for multimodal foundation models into a pyramid, focusing on real-robot, UMI-style, egocentric and exocentric, simulation, and general vision-language data.

This paper proposes an ontological representation for cooperative affordances in social robotics, enabling agents to extend their action possibilities through interaction.

This paper proposes robot-factored world models for action-conditioned video prediction in robotics, which factor out action realization and robot rendering to avoid learning the realization process a…

This paper introduces Camera-Centric VLA, a new model for Vision-Language-Action policies that predicts camera-centric actions and hand-eye matrix, allowing the policy to figure out camera geometry on…

The paper introduces BayesContact, a framework for visuo-tactile pose estimation using simulation-based inference, improving pose observability and insertion success by 30%.

This paper systematically analyzes 48 studies on perception attacks against autonomous vehicles, revealing that the increasing reliance on multi-sensor fusion creates new, complex vulnerabilities that…

The paper introduces RoboTrustBench, a comprehensive benchmark that evaluates the trustworthiness of video world models for robotic manipulation across challenging scenarios, finding that current mode…

The paper introduces a diagnostic framework to determine if World-Action Models (WAMs) provide genuinely actionable behavioral improvements beyond simply achieving task success, finding that WAMs ofte…

The paper presents ViTacWorld, a framework for scalable contact-rich robot manipulation using a visuo-tactile world model.

The paper presents RoboSpatialBrain, a system that combines two mechanisms to improve spatial reasoning in vision-language models for embodied tasks, achieving first place in the RoboSpatial Challenge…

This paper provides a comparative framework analyzing the distinct security and privacy risks inherent in virtual and robotic assistive systems, culminating in design recommendations for trustworthy t…

This paper demonstrates that typographic attacks pose a significant, measurable, and physically consequential threat to household robot manipulation systems by causing the robot to grasp and transport…

The paper presents SenseNova-Vision, a unified multimodal model for computer vision tasks using natural language instructions and optional visual prompts, trained primarily on a new corpus and requiri…