Embodied Agents

Tianle Zhang, Zhihao Yuan, Dafeng Chi et al.

Introduces JoyAI-RA, a vision-language-action foundation model that enhances robotic autonomy through improved generalization across diverse robotic embodiments and tasks.

Yupeng Zheng, Xiang Li, Songen Gu et al.

Presents a lightweight VLA model with world knowledge integration for efficient robot manipulation, enhancing spatial reasoning and task execution in compact robotic systems.

Tao Zhang, Kaixian Qu, Zhibin Li et al.

DESPITE reveals that even highly accurate LLM planners can systematically fail safety-critical tasks, exposing a critical gap between planning accuracy and real-world safety—essential for deploying robots in human environments.

Hikaru Shindo, Hanzhao Lin, Lukas Helff et al.

SocialGrid provides the first benchmark for social reasoning in embodied multi-agent systems, exposing critical gaps in LLM agents' planning and deception detection—essential for building trustworthy autonomous agents.

Nastaran Darabi, Amit Ranjan Trivedi

ProGAL-VLA adds verified grounding and prospective sub-goals to VLA robots, sharply improving instruction sensitivity, ambiguity handling, and robustness under perturbation.

Yunsong Zhou, Hangxu Liu, Xuekun Jiang et al.

SIM1 builds physics-aligned real-to-sim twins for deformable manipulation, letting purely synthetic training reach real-data parity at a fraction of collection cost and making sim-scaled robotics learning much more practical.

Jiajun Zhai, Hao Shi, Shangwei Guo et al.

E-VLA uses event cameras—normally used in robotics—to let robots see and act in near-total darkness or blur, where normal cameras fail. This enables real-world robotic systems to operate reliably in challenging environments like smoke-filled rooms or fast-moving scenes.