💡 The paper introduces SkillNet, an open infrastructure designed to systematically accumulate and transfer artificial intelligence skills across multiple domains. The problem addressed is that current AI agents lack a unified mechanism for skill consolidation, resulting in redundant efforts and limited long-term advancement. To overcome this limitation, SkillNet structures skills within a unified ontology that supports creating skills from diverse sources, establishing connections, and evaluating skills across multiple dimensions such as safety, completeness, and cost awareness.

The SkillNet infrastructure consists of a repository of over 200,000 skills, an interactive platform, and a Python toolkit. This infrastructure enables the creation, evaluation, and organization of AI skills at scale. By formalizing skills as evolving and composable assets, SkillNet provides a robust foundation for agents to move from transient experience to durable mastery.

The results of the paper demonstrate the effectiveness of SkillNet in enhancing agent performance. Experimental evaluations on various environments such as ALFWorld, WebShop, and ScienceWorld show that SkillNet significantly improves average rewards by 40 percent and reduces execution steps by 30 percent across multiple backbone models. Overall, the paper contributes to the development of a unified infrastructure for AI skill accumulation and transfer, which has the potential to accelerate the advancement of AI agents across multiple domains.

💡 The paper introduces Embodied-R1.5, a unified embodied foundation model that integrates various embodied reasoning capabilities, such as cognition, task planning, correction, and pointing, into a single architecture. The goal is to achieve general physical intelligence. To train the model, the authors developed three automated data construction pipelines, resulting in a large-scale data system of over 15 billion tokens. They also designed a multi-task balanced reinforcement learning approach to alleviate conflicts between different tasks.

The model consists of a Planner-Grounder-Corrector framework, which enables it to autonomously execute and self-correct over long-horizon tasks. With only 8 billion parameters, Embodied-R1.5 achieves state-of-the-art performance on 16 out of 24 embodied vision-language benchmarks, surpassing leading models. The model can also be fine-tuned into a vision-language agent with a small amount of data, outperforming leading models across popular manipulation benchmark suites.

The authors conducted extensive zero-shot real-robot experiments, demonstrating the model's strong generalization to the physical world. The experiments validated the model's performance in instruction following, affordance grounding, articulated object manipulation, and long-horizon complex tasks. The paper's contributions include the introduction of the Embodied-R1.5 model, the development of a large-scale data system, and the creation of an evaluation framework tailored for embodied tasks. The model weights, datasets, training code, and evaluation framework are open-sourced to facilitate future research in embodied foundation models.

The problem addressed in the paper is the development of a unified embodied foundation model that can achieve general physical intelligence. The method used to address this problem is the integration of various embodied reasoning capabilities into a single architecture, along with the development of a large-scale data system and a multi-task balanced reinforcement learning approach. The results show that Embodied-R1.5 achieves state-of-the-art performance on various benchmarks and demonstrates strong generalization to the physical world. Overall, the paper contributes to the development of embodied foundation models and has the potential to facilitate future research in this area.