Publikation
Adaptive Model-Based Control of Quadrupeds via Online System Identification using Kalman Filter
Jonas Haack; Franek Stark; Shubham Vyas; Frank Kirchner; Shivesh Kumar
In: 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-2025), October 19-25, Hangzhou, China, Pages 5039-5044, IEEE, 2025.
Zusammenfassung
Many real-world applications require legged robots to be able to carry variable payloads. Model-Based controllers such as model predictive control (MPC) have become the de facto standard in research for controlling these systems. However, most model-based control architectures use fixed plant models, which limits their applicability to different tasks. In this paper, we present a Kalman filter (KF) formulation for online identification of the mass and center of mass (COM) of a four-legged robot. We evaluate our method on a quadrupedal robot carrying various payloads and find that it is more robust to strong measurement noise than classical recursive least squares (RLS) methods. Moreover, it improves the tracking performance of the model-based controller with varying payloads when the model parameters are adjusted at runtime.
Projekte
- AAPLE - Expanding the Action-Affordance Envelope for Planetary Exploration using Dynamics Legged Robots
- CoEx - Entwicklung von Methoden zur Co-Adaptation für die Ermöglichung und Verbesserung von Exoskelett-basiertee (Tele-) Rehabilitation
- ActGPT - Adaptive robot ConTrol with Generative Pre-trained Transformers