# Bayesian Multi-Task Learning MPC for Robotic Mobile Manipulation

arXiv: https://arxiv.org/abs/2211.10270 · HTML: https://mlsanity.com/paper/2211.10270/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2022.

## Built on by (most cited first, 16 of 16)

- [Robot Parkour Learning](https://mlsanity.com/paper/2309.05665/) (2023)
- [Visual Whole-Body Control for Legged Loco-Manipulation](https://mlsanity.com/paper/2403.16967/) (2024)
- [Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems](https://mlsanity.com/paper/2306.13867/) (2023)
- [Learning to Open and Traverse Doors with a Legged Manipulator](https://mlsanity.com/paper/2409.04882/) (2024)
- [Learning Whole-Body Loco-Manipulation for Omni-Directional Task Space Pose Tracking with a Wheeled-Quadrupedal-Manipulator](https://mlsanity.com/paper/2412.03012/) (2024)
- [Adapting World Models with Latent-State Dynamics Residuals](https://mlsanity.com/paper/2504.02252/) (2025)
- [WildLMa: Long Horizon Loco-Manipulation in the Wild](https://mlsanity.com/paper/2411.15131/) (2024)
- [Versatile Loco-Manipulation through Flexible Interlimb Coordination](https://mlsanity.com/paper/2506.07876/) (2025)
- [LiRA: Light-Robust Adversary for Model-based Reinforcement Learning in Real World](https://mlsanity.com/paper/2409.19617/) (2024)
- [ZipMPC: Compressed Context-Dependent MPC Cost via Imitation Learning](https://mlsanity.com/paper/2507.13088/) (2025)
- [Context-Aware Deep Lagrangian Networks for Model Predictive Control](https://mlsanity.com/paper/2506.15249/) (2025)
- [Attacking All Tasks at Once Using Adversarial Examples in Multi-Task Learning](https://mlsanity.com/paper/2305.12066/) (2023)
- [Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning](https://mlsanity.com/paper/2307.00828/) (2023)
- [A Reactive Framework for Whole-Body Motion Planning of Mobile Manipulators Combining Reinforcement Learning and SDF-Constrained Quadratic Programmi](https://mlsanity.com/paper/2503.23975/) (2025)
- [Exact and general decoupled solutions of the LMC Multitask Gaussian Process model](https://mlsanity.com/paper/2310.12032/) (2023)
- [Meta-Learning and Meta-Reinforcement Learning -- Tracing the Path towards DeepMind's Adaptive Agent](https://mlsanity.com/paper/2602.19837/) (2026)

## Abstract (arXiv)

Mobile manipulation in robotics is challenging due to the need of solving many diverse tasks, such as opening a door or picking-and-placing an object. Typically, a basic first-principles system description of the robot is available, thus motivating the use of model-based controllers. However, the robot dynamics and its interaction with an object are affected by uncertainty, limiting the controller's performance. To tackle this problem, we propose a Bayesian multi-task learning model that uses trigonometric basis functions to identify the error in the dynamics. In this way, data from different but related tasks can be leveraged to provide a descriptive error model that can be efficiently updated online for new, unseen tasks. We combine this learning scheme with a model predictive controller, and extensively test the effectiveness of the proposed approach, including comparisons with available baseline controllers. We present simulation tests with a ball-balancing robot, and door-opening hardware experiments with a quadrupedal manipulator.