GRASP on Robotics is an inaugural series of talks hosted by the GRASP Laboratory. GRASP leverages academic, research, and industry connections to deliver a set of high class tech talks with the mission of providing technical topics and meaningful discussions.
Join the live-stream here Fridays from 10:30am -11:45am followed by a Q&A panel between our speaker, faculty, and students until 11:45am.
Fall 2025 GRASP on Robotics: Jan Peters, Technische Universität Darmstadt & German Research Center for Artificial Intelligence, “Inductive Biases for Robot Learning”
This is a hybrid event with in-person attendance in Wu & Chen auditorium and virtual attendance via Zoom.
ABSTRACT
The quest for intelligent robots capable of learning complex behaviors from limited data hinges critically on the design and integration of inductive biases—structured assumptions that guide learning and generalization. In this talk, Jan Peters explores the foundational role of inductive biases in robot learning, drawing from insights in control theory, neuroscience, and machine learning. He discusses how exploiting physical principles, modular control structures, symmetry, temporal abstraction, and domain-specific priors can drastically reduce sample complexity and improve robustness in robotic systems.
Through a series of concrete examples—including robot table tennis, tactile manipulation, quadruped locomotion, and dynamic motor skill learning on anthropomorphic arms—Peters illustrates how inductive biases enable efficient policy search, reinforcement learning, and imitation learning. These applications demonstrate how embedding prior knowledge about motor primitives, control hierarchies, or contact dynamics helps robots acquire versatile skills with minimal data. The talk concludes with a vision for future robot learning systems that integrate such structured biases with modern data-driven methods, enabling scalable, adaptive, and generalizable autonomy in real-world environments.