Research

Meta-Learning Augmented MPC for Disturbance-Aware Motion Planning and Control of Quadrotors

Dzenan Lapandic, Fengze Xie, Christos K. Verginis, Soon-Jo Chung, Dimos V. Dimarogonas, Bo Wahlberg

[paper]

 A major challenge in autonomous flights is unknown disturbances, which can jeopardize safety and cause collisions, especially in obstacle-rich environments. This paper presents a disturbance-aware motion planning and control framework for autonomous aerial flights. The framework is composed of two key components: a disturbance-aware motion planner and a tracking controller. The motion planner consists of a predictive control scheme and an online-adapted learned disturbance model. The tracking controller, developed using contraction control methods, ensures safety bounds on the quadrotor’s behavior near obstacles with respect to the motion plan. The algorithm is tested in simulations with a quadrotor facing strong crosswind and ground-induced disturbances.

MAGIC-VFM - Meta-learning Adaptation for Ground Interaction Control with Visual Foundation Models

Elena Sorina Lupu*, Fengze Xie*, James A. Preiss, Matthew Anderson, Jedidiah Alindogan, Soon-Jo Chung

[paper]

Planning and control of ground vehicles are challenging because of complex dynamic interactions with the terrain.

Therefore, accurate modeling of terrain interaction forces is important to optimize their driving performance.

We present an offline meta-learning algorithm to construct a rapidly-tunable model of residual dynamics and disturbance using both visual foundation models and vehicle states.

This model is then integrated with composite adaptive control for the control matrix to adapt to changes in both the terrain and vehicle dynamics conditions in real time.

Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for Robotic Dynamics Learning

Fengze Xie*, Sizhe Wei*, Yue Song, Yisong Yue, Lu Gan

[paper]

We present a morphological-symmetry-equivariant heterogeneous graph neural network, namely MS-HGNN, for robotic dynamics learning that integrates robotic kinematic structures and morphological symmetries into a single graph network. These structural priors are embedded into the learning architecture as constraints, ensuring high generalizability, sample, and model efficiency. The proposed MS-HGNN is a versatile and general architecture that is applicable to various multi-body dynamic systems and a wide range of dynamics learning problems. We formally prove the morphological-symmetry-equivariant property of our MS-HGNN and validate its effectiveness across multiple quadruped robot learning problems using both real-world and simulated data. 

Online Policy Optimization in Unknown Nonlinear Systems

Yiheng Lin, James A. Preiss, Fengze Xie, Emile Anand, Soon-Jo Chung, Yisong Yue, Adam Wierman

[paper]

We study online policy optimization in nonlinear time-varying dynamical systems where the true dynamical models are unknown to the controller. This problem is challenging because unlike in linear systems, the controller cannot obtain globally accurate estimations of the ground-truth dynamics using local exploration. We propose a meta-framework that combines a general online policy optimization algorithm with a general online estimator of the dynamical system’s model parameters.

Hierarchical Meta-learning-based Adaptive Controller

Fengze Xie, Guanya Shi, Michael O'Connell, Yisong Yue, Soon-Jo Chung

[paper], [website]

This paper presents the Hierarchical Meta-learning-based Adaptive Controller (HMAC) to learn and adapt to such multi-source disturbances. 

HMAC handles both manageable and latent disturbances with hierarchical iterative learning and smoothed streaming meta-learning. 

Improved Autonomy with Rapidly Learned Dynamics from Adaptive Control

John Lathrop, James Preiss, Elena-Sorina Lupu, Fengze Xie, Soon-Jo Chung

We study the benefits of incorporating a fast-adapting online dynamics model into the robotic autonomy pipeline.

A composite adaptive control law updates the parameters of a residual dynamics model, simultaneously minimizing the disturbance prediction error and the tracking error of the planned trajectory with exponential convergence.

Breaking the strict separation between planning and control, we incorporate online dynamics updates in our motion planner.

Joint-Space Multi-Robot Motion Planning with Learned Decentralized Heuristics

Fengze Xie, Marcus Dominguez-Kuhne, Benjamin Riviere, Jialin Song, Wolfgang Hönig, Soon-Jo Chung, Yisong Yue

[paper]

In this paper, we present a method of multi-robot motion planning by biasing centralized, sampling-based tree search with decentralized, data-driven steer and distance heuristics.