ICML 2025

Learning Dynamics under Environmental Constraints via Measurement-Induced
Bundle Structures
Dongzhe Zheng 1 Wenjie Mei 2
Abstract
Learning unknown dynamics under environmen-
tal (or external) constraints is fundamental to
many fields (e.g., modern robotics), particularly
challenging when constraint information is only
locally available and uncertain.
Existing ap-
proaches requiring global constraints or using
probabilistic filtering fail to fully exploit the geo-
metric structure inherent in local measurements
(by using, e.g., sensors) and constraints. This
paper presents a geometric framework unifying
measurements, constraints, and dynamics learn-
ing through a fiber bundle structure over the state
space. This naturally induced geometric struc-
ture enables measurement-aware Control Barrier
Functions that adapt to local sensing (or measure-
ment) conditions. By integrating Neural ODEs,
our framework learns continuous-time dynam-
ics while preserving geometric constraints, with
theoretical guarantees of learning convergence
and constraint satisfaction dependent on sensing
quality. The geometric framework not only en-
ables efficient dynamics learning but also sug-
gests promising directions for integration with re-
inforcement learning approaches. Extensive sim-
ulations demonstrate significant improvements in
both learning efficiency and constraint satisfac-
tion over traditional methods, especially under
limited and uncertain sensing conditions.
1. Introduction
Learning unknown dynamics under measurement con-
straints is fundamental to many applications, from manipu-
lators with force sensing to autonomous vehicles with range
1Department of Computer Science and Engineering, Shanghai
Jiao Tong University, Shanghai, China 2School of Automation
and Key Laboratory of MCCSE of Ministry of Education, South-
east University, Nanjing, China. Correspondence to: Wenjie Mei
<wenjie.mei@seu.edu.cn; vmus1130@gmail.com>.
Proceedings of the 42 nd International Conference on Mach...