Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models
arXiv:2606. 11324v1 Announce Type: cross Abstract: We introduce Embodied-R1.
arXiv:2606. 11324v1 Announce Type: cross Abstract: We introduce Embodied-R1.
Building capable embodied agents requires not only multimodal perception and understanding, but also agentic capabilities for reasoning about actions, adapting to evolving situations, and interacting with the physical world. In this report, we introduce Hy-Embodied-VLM-1.
arXiv:2610.18836v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) have achieved strong performance in visual perception and spatial reasoning, leading to growing interest in using the...
arXiv:2608. 06756v1 Announce Type: new Abstract: Vision-language models are increasingly serving as the reasoning core of embodied agents.
arXiv:2608.22971v2 Announce Type: replace Abstract: Embodied AI has emerged as a key paradigm for autonomous systems operating in complex physical environments. While end-to-end models have establish...
arXiv:2607. 10350v1 Announce Type: new Abstract: Recent VLM and VLA systems have improved robotic perception and action prediction, yet long-horizon embodied agents still require a general runtime layer for reasoning, memory, tool use, verification, and cross-embodiment execution.
arXiv:2607. 18985v2 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in language understanding, reasoning, and world knowledge.
arXiv:2607. 18985v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in language understanding, reasoning, and world knowledge.
arXiv:2608. 19794v1 Announce Type: new Abstract: The convergence of large language models (LLMs), structured knowledge bases (KBs), and reasoning ability (RA) presents a promising trajectory toward general embodied intelligence (GEI).
arXiv:2607. 03449v1 Announce Type: cross Abstract: Current Vision-Language-Action (VLA) models excel at robotic manipulation but often struggle with non-Markovian tasks requiring long-term memory and reasoning due to their reliance on immediate observations.
arXiv:2609.24526v1 Announce Type: new Abstract: Physical AI requires models to ground visual and linguistic understanding in real-world environments while accounting for environmental constraints and...
arXiv:2606. 18363v1 Announce Type: cross Abstract: Language models trained on large-scale vision-language data have demonstrated strong potential for embodied agents.