The paper introduces an automated design method for soft-rigid hybrid robots, optimizing the structure to achieve effective dynamic locomotion, demonstrated by successful walking experiments.

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…

This paper studies how to scale robust robot policies by expanding physical domains in a recoverable way.

The paper proposes a robust sim-to-sim-to-real DRL approach to enable double-Ackermann robots to achieve full pose control despite significant actuation uncertainties and discrepancies between simulat…

The paper introduces using frozen, generalist value functions as differentiable surrogates to efficiently optimize and analyze new multi-embodiment robot designs without requiring repeated reinforceme…

PhyGenHOI introduces a novel framework that generates physically accurate and visually faithful 4D Human-Object Interactions by coupling generative human motion with explicit physical object simulatio…

The paper proposes an iCEM+TL framework that combines the Sample-efficient Cross-Entropy Method with Transfer Learning and Reward Redesign to improve robotic motion planning for complex tasks like sta…

This paper presents GRAIL, a digital generation pipeline that synthesizes human-object interactions for humanoid robots.

The paper proposes a novel framework to visualize and uncover latent, structured motion phases in deep reinforcement learning locomotion policies by augmenting state observations with action and next-…

This survey synthesizes the state-of-the-art in AI-IoT-Robotics integration, proposing a modular architecture and highlighting hybrid SLM-LLM systems as the path toward next-generation Connected Robot…

The paper demonstrates that fine-tuning safety guard models on benign data can catastrophically collapse their safety alignment, proposing Fisher-Weighted Safety Subspace Regularization (FW-SSR) to ac…

This paper presents Mana, a sim-to-real framework for dexterous articulated tool manipulation.

The paper proposes CTRL-STEER, a closed-loop framework that adaptively adjusts intervention strength to stabilize concept regulation and improve task success in Vision-Language-Action models without r…

The paper proposes a novel attack paradigm demonstrating how compromising a single robot in an LLM-controlled multi-robot system can rapidly propagate malicious intent to cause coordinated unsafe acti…

Restructuring the communication topology among robots provides significantly greater performance gains in multi-robot coordination than simply increasing the size of the onboard AI models, given fixed…

This survey provides a comprehensive, structured review of safety research in Embodied AI, analyzing attacks and defenses across the entire embodied pipeline to guide the development of safe, robust,…

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…