30 credits – Embodiment-Aware Autonomous Driving Policies with Deep Learning
A Master's thesis is an excellent way to get closer to TRATON Group R&D and build relationships for the future.
Recent advances in autonomous driving have been largely driven by large-scale imitation learning (IL) from human driving data. By leveraging large datasets of expert demonstrations, learned driving policies have achieved impressive performance across a wide range of driving scenarios. A key challenge for autonomous trucking is that vehicle configurations differ substantially in geometry, articulation, axle layout, payload capacity, braking authority, and steering constraints. These differences influence feasible driving behaviors and motion-planning decisions. While separate policies can be trained for each vehicle configuration, such an approach scales poorly as fleets become increasingly diverse. Conversely, learning a shared policy across multiple vehicle types is challenging because embodiment-specific behaviors must be preserved while still leveraging common driving knowledge.
Similar challenges have recently been studied in robotics under the umbrella of multi-embodiment learning. Prior work has shown that conditioning policies on robot morphology, kinematics, or learned dynamics representations can improve transfer across heterogeneous platforms1,2. These ideas suggest that embodiment-aware learning may provide a scalable approach for developing autonomous driving policies that generalize across diverse vehicle configurations while adapting to their unique constraints.
Inspired by related works3, this thesis investigates how vehicle-configuration-aware driving policies can be learned from heterogeneous driving data. In particular, the work will explore how policies trained on passenger-vehicle data can be adapted to heavy-duty vehicle configurations, whether embodiment conditioning improves generalization across vehicle platforms, and how training on diverse multi-vehicle datasets influences policy performance and behavior.
The goal of this thesis is to investigate the learning of vehicle-configuration-aware driving policies using imitation learning and embodiment conditioning. The assignment is divided into several subtasks:
Master (civilingenjör) in computer science, robotics, electrical engineering, or applied mathematics, preferably with specialization in artificial intelligence algorithms.
Number of students: 1-2
Start date: January 2027
Estimated time needed: 20 weeks
Caroline Skoglund, Industrial PhD Student (KTH Robotics, Perception and Learning) caroline.skoglund@scania.com
Yunus Emre Sahin, Research & Development Engineer in Autonomous Motion yunus-emre.sahin@scania.com
Hiring Manager: Magnus Granström, magnus.granstrom@scania.com
Your application must include a CV, cover letter and transcript of grades.
A background check might be conducted for this position. We are conducting interviews continuously and may close the recruitment earlier than the date specified.
References
[1] K. Bousmalis et al., “RoboCat: A self-improving generalist agent for robotic manipulation,” Trans. Mach. Learn. Res., 2024.
[2] A. Gupta, L. Fan, S. Ganguli, and L. Fei-Fei, “MetaMorph: Learning universal controllers with transformers,” in Proc. Int. Conf. Learn. Representations (ICLR), 2022.
[3] H. Oh and J. Park, “MVAdapt: Zero-shot multi-vehicle adaptation for end-to-end autonomous driving,” 2026. [Online]. Available: https://arxiv.org/abs/2604.11854
Publication date:
1.10.2026 - 30.11.2026 (applications evaluated continuously)
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