PhD Position on Agentic and System-1/System-2 Architectures for Robot Autonomy
Research Title:
Agentic and System-1/System-2 Architectures for Robot Autonomy
Supervisors:
Daniele Gammelli, Giuseppe Averta
Topics:
Robotics, Machine Learning, Foundation Models, Vision-Language-Action Models, World Models
Funded by:
The Italian Institute of Artificial Intelligence (AI4I), in collaboration with Politecnico di Torino
Group Website:
https://mira.rd-labs.ai4i.it/
https://danielegammelli.github.io
giuseppeaverta.me
Summary of the PhD project:
The Italian Institute of Artificial Intelligence for Industry (AI4I), in collaboration with Politecnico di Torino, invites applications for a PhD position focused on Agentic and System-1/System-2 Architectures for Robot Autonomy.
This research project investigates how agentic architectures built on multimodal foundation models, Vision-Language-Action (VLA) policies, world models, and model-based controllers can be designed and orchestrated to achieve reliable robot autonomy in unstructured environments. Research will focus on both standalone agentic policies and on System-1/System-2 architectures, in which a slow, deliberative reasoning module (System 2) plans, monitors, and recovers from failures, while a fast, reactive policy (System 1) executes motor commands under real-time and safety constraints. The project focuses on how reasoning, planning, and control should be distributed across these levels, how they should communicate, and how the resulting systems can improve from deployment experience.
Research Objectives and Methods:
Recent advances in multimodal foundation models, VLA models, and learned world models are reshaping the design of autonomous robots. Emerging architectures increasingly move beyond fixed perception–planning–control pipelines, combining large-scale models with specialized policies, tools, memory, and model-based components. However, foundation models are largely trained on data and objectives that do not capture multimodal feedback, real-time constraints, embodiment-specific dynamics, and safety requirements that are central to robotics.
This project studies agentic robotic architectures that exploit the flexibility of foundation models while explicitly accounting for physical constraints, uncertainty, and the limitations of learned components. A natural instantiation is the System-1/System-2 paradigm: a high-level vision-language reasoner decomposes tasks, tracks progress, and decides when and how to intervene, while a low-level VLA or learned skill policy, possibly coupled with optimal control or model predictive control, produces fast and physically consistent actions. Key open questions concern the interface between the two systems (e.g., language, latent plans, subgoals, or trajectories), their different time scales, when the slow system should be invoked, and how failures detected at one level should be handled at the other.
The developed methods will be studied in manipulation and humanoid robotics, with possible extensions to other autonomy domains, and evaluated both in simulation and on real robotic platforms.
Research objectives:
- characterize the capabilities and failure modes of agentic systems for robotics, with emphasis on physical reasoning, long-horizon planning, task completion assessment, failure recovery, and real-time constraints.
- design System-1/System-2 architectures that couple deliberative reasoning models with fast VLA policies and learned skills, studying their interfaces, invocation strategies, and time-scale separation.
- investigate hybrid approaches combining learned components with world models, trajectory optimization, model predictive control, and explicit safety mechanisms.
- study how robotic systems can improve from deployment experience through continual learning, targeted data collection, and data flywheels triggered by failures or novel situations.
- evaluate in simulation and on real robotic platforms, focusing on long-horizon task success, robustness, failure recovery, and data efficiency.
The research work plan will be articulated in four main phases:
First, the candidate will review the literature on agentic AI, VLA models, world models, and dual-process (System-1/System-2) architectures for embodied AI, and will systematically benchmark current systems to characterize their capabilities and failure modes in physical tasks.
Second, the candidate will develop System-1/System-2 architectures in which a multimodal reasoning model orchestrates one or more VLA policies, learned skills, and model-based controllers. This phase will focus on the representation exchanged between levels, on deciding when deliberation is needed, and on keeping low-level execution reactive and safe.
Third, the candidate will investigate verification and failure-recovery mechanisms across the stack, as well as offline uses of agentic systems to support simulation, data generation, evaluation, and policy improvement, closing the loop through data flywheels driven by deployment experience.
Fourth, the developed methods will be validated in simulation and on real platforms in laboratory settings, considering long-horizon task success, robustness to novel situations, recovery from failures, computational latency, and the amount of supervision or robot data required.
The research will be conducted in collaboration between the MIRA Laboratory at the Italian Institute of Artificial Intelligence for Industry (AI4I) and the Vandal Lab at the Politecnico di Torino, with access to AI4I’s Peano computing infrastructure and opportunities for research visits at international partner institutions (e.g., Stanford University, MIT, ETH Zurich). We plan to publish outcomes of this research to premium conferences like CoRL, RSS, ICRA, IROS, NeurIPS, ICML, and ICLR, as well as journals such as IEEE Robotics and Automation Letters, IEEE Transactions on Robotics, and The International Journal of Robotics Research. The candidate will also have the opportunity to join the activities of the Ellis Unit Turin, and apply to be an Ellis PhD Student under our supervision.
Required Skills and Eligibility:
Candidates should have a strong background in machine learning, robotics, computer vision, or control, together with solid programming skills in Python and experience with deep learning frameworks such as PyTorch or JAX. Independence, research motivation, and strong analytical skills are essential.
Knowledge of foundation models, Vision-Language-Action models, reinforcement learning, imitation learning, or world models is highly desirable. Experience with robotic systems, ROS/ROS 2, simulation environments such as MuJoCo or Isaac Sim, and optimal control or model predictive control is an advantage.
Applicants must also satisfy Politecnico di Torino’s formal PhD admission requirements:
- Academic qualification: For qualifications obtained in Italy, a Master’s degree (Laurea Magistrale or Laurea Specialistica), or an eligible degree under the previous Italian university system, with a minimum final grade of 95/110. For qualifications obtained abroad, a degree that grants access to PhD studies in the country where it was awarded, meeting the minimum average examination grade specified in the relevant call for applications.
- English proficiency: An accepted English language certificate at CEFR level B2, unless an exemption specified in the call applies.
- Completion deadlines: Candidates who have not yet obtained the required degree and/or English language certificate may apply, provided they fulfil these requirements by the deadlines specified in the call.
Full eligibility conditions, accepted language certificates, exemptions, and deadlines are governed by the applicable call for applications. Please consult Politecnico di Torino’s official admission requirements.
Expression of Interest and Formal Application:
To express your interest in this PhD opportunity, please submit the following:
- Curriculum vitae (CV) detailing your education, research experience, and relevant technical skills.
- Academic records, including degree diplomas and transcripts for your Bachelor’s and Master’s studies. If your Master’s degree is ongoing, please include your latest transcript and expected graduation date.
- Publication list, if applicable.
- Contact details for at least two referees, including their names, affiliations, email addresses, and relationship to you. Reference letters are not required at this stage.
This announcement is a preliminary expression of interest. Submitting these materials does not constitute a formal application to Politecnico di Torino’s PhD programme or guarantee admission. Candidates must also submit a formal application through the official Apply@polito platform, following the requirements and deadlines of the applicable call.
The currently open admission round is the second session of the 42nd PhD cycle, with an application deadline of 16 November 2026 at 14:00 CET. Details of the session and available positions are published on Politecnico di Torino’s official call for applications page.
About Us
The Italian Institute for Artificial Intelligence (AI4I) was founded as a research institute to perform transformative, application-oriented research in Artificial Intelligence, driving innovation and industrial progress. The Institute is designed to engage and empower gifted, entrepreneurial, and ambitious researchers who are committed to generating real-world impact at the intersection of science, technology, and industrial transformation.
Competitive salaries, performance-based incentives, access to dedicated high-performance computing resources, state-of-the-art laboratories, and strong industrial collaborations are among the distinctive features that define AI4I. The Institute fosters a dynamic international environment and an ecosystem that supports the creation and growth of innovative startups.
AI4I’s mission is to advance scientific research, technology transfer, and, more broadly, Italy’s innovation capacity, promoting positive impact across industry, services, and public administration. To achieve this, the Institute contributes to building a research and innovation infrastructure that leverages AI methods, with a special focus on manufacturing processes and the broader Industry 4.0 value chain.
AI4I also maintains strategic relationships with leading organizations in Italy and abroad, including Competence Centers and European Digital Innovation Hubs (EDIHs), positioning itself as an attractive destination for researchers, companies, and startups seeking collaboration and impact.
AI4I is an equal opportunity employer and welcomes applications from all qualified candidates regardless of gender, nationality, ethnicity, religion, disability, age, or sexual orientation.