$π_0$: A Vision-Language-Action Flow Model for General Robot Control

Robot learning holds tremendous promise to unlock the full potential of flexible, general, and dexterous robot systems, as well as to address some of the deepest questions in artificial intelligence. However, bringing robot learning to the level of generality required for effective real-world systems faces major obstacles in terms of data, generalization, and robustness. In this paper, we discuss how generalist robot policies (i.e., robot foundation models) can address these challenges, and how we can design effective generalist robot policies for complex and highly dexterous tasks. We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge. We then discuss how this model can be trained on a large and diverse dataset from multiple dexterous robot platforms, including single-arm robots, dual-arm robots, and mobile manipulators. We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people and from a high-level VLM policy, and its ability to acquire new skills via fine-tuning. Our results cover a wide variety of tasks, such as laundry folding, table cleaning, and assembling boxes.

Organization of ML-based product development as per ISO 26262

Machine learning (ML) applications generate a continuous stream of success stories from various domains. ML enables many novel applications, also in a safety-related context. With the advent of Autonomous Driving, ML gets used in automotive domain. In such a context, ML-based systems are safety-related. In the automotive industry, the applicable functional safety standard is ISO 26262, which it does not cover specific aspects of ML. In a safety-related ML project, all ISO 26262 work products are typically necessary and have to be delivered. However, specific aspects of ML (like data set requirements, special analyses for ML) must be addressed within some work products. In this paper, we propose how the organization of a ML project could be done according to ISO 26262 phases, sub-phases and work-products.