PhD Position on Trustworthy Generalist Robot Policies
Deadline: 16 November 2026 at 14:00 CET
Research title: Trustworthy Generalist Robot Policies
Supervisors: Daniele Gammelli, Tatiana Tommasi, Raffaello Camoriano
Topics: Robotics, Machine Learning, Foundation Models, Vision-Language-Action Models, World Models
Funding: Italian Institute of Artificial Intelligence (AI4I), in collaboration with Politecnico di Torino
Group and supervisor websites
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 Trustworthy Generalist Robot Policies.
This research will investigate how generalist robotic systems leveraging Foundation Models (FMs) can be equipped with explicit mechanisms to ensure trustworthy autonomy. It will develop methods to support safe, reliable, and efficient operation while enabling continuous adaptation to novel real-world scenarios and maintaining runtime assurance without compromising generalization and flexibility. The research will focus on trustworthy learned representations, safety specifications, uncertainty estimation, runtime monitoring, and model-based intervention. The proposed methods will be developed and evaluated in representative settings, including autonomous navigation, mobile manipulation, and whole-body robotic systems.
Research Objectives and Methods
The rapid advancement of robot learning is enabling generalist policies that leverage Foundation Models and large, heterogeneous datasets, to operate across diverse tasks, objects, environments, and robotic embodiments. However, generalization alone does not guarantee trustworthy autonomy: models optimized for task performance may fail to satisfy explicit safety and operational constraints, particularly in scenarios beyond their training distribution. Reliable deployment requires more than successful task execution.
As robotic systems increasingly rely on learned policies, world models, and multimodal reasoning modules, to estimate risks and support decision-making, a key challenge is determining when their predictions, representations, and intermediate reasoning signals can be trusted in safety-critical settings. Beyond predictive accuracy, trustworthy autonomy requires learned components to faithfully capture relevant system dynamics and causal relationships rather than rely on spurious correlations, and to ensure that generated explanations meaningfully reflect the reasoning underlying the actions ultimately executed. These capabilities are essential for trustworthy autonomous operation in real-world environments.
Research objectives:
- Characterize and diagnose failures of generalist robotic systems. Investigate the reliability of learned components by assessing whether their predictions, representations, plans, and reasoning signals are consistent with observations, system dynamics, and executed actions, particularly in novel and safety-critical scenarios.
- Formalize trustworthiness requirements. Develop methods to specify and evaluate safety, robustness, and efficiency requirements, accounting for geometric, dynamical, semantic, and task-dependent constraints.
- Develop runtime assurance mechanisms. Design methods to detect unsafe or unreliable behavior and intervene when necessary by rejecting, modifying, or replacing actions, providing formal safety guarantees or measurable risk reduction while preserving policy generalization.
- Enable continual improvement of safety and reliability. Investigate how failures, near-misses, and uncertain situations can inform targeted data collection, policy refinement, and improved safety mechanisms, enabling adaptation to new tasks and environments while maintaining safe operation.
The research work plan will be articulated in four main phases.
First, the candidate will review the literature on Foundation Models for robotic systems (VLA, World Models) and architectures for embodied AI and will systematically benchmark current systems to characterize their capabilities and failure modes in physical tasks.
Second, the candidate will study reliability and faithfulness of learned components used for decision making with methods based on on counterfactual interventions, consistency tests, uncertainty estimation, representation analysis, and targeted post-training to identify cases in which apparently plausible predictions or explanations fail to provide reliable evidence about system behavior.
Third, the candidate will develop approaches that combine learned representations with known physical structures, analytical models and explicity constraints to enforce trustrworthy operation. Possible approaches will combine learned safety signals with predictive models, constrained optimization, reachability analysis, or control barrier functions to reject, modify, or replace unsafe actions while preserving the generalization and adaptability of the underlying learned policy.
Fourth, the developed methods will be extended to continual learning settings and validated in simulation as well as on real platforms in laboratory settings, considering long-horizon task success, robustness to novel situations, failure risk reduction, computational latency, and the amount of supervision or robot data required.
The research will be conducted in collaboration with the MIRA Laboratory at the Italian Institute of Artificial Intelligence for Industry (AI4I), 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.
Please also note that applications submitted through AI4I do not replace the official application process. Candidates must also apply through the official Politecnico di Torino PhD call portal, as indicated in the position description.
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.