Model Predictive Robot-Environment Interaction Control for Mobile Manipulation Tasks

Minniti, Maria Vittoria; Grandia, Ruben; Farshidian, Farbod; Hutter, Marco

doi:10.1109/icra48506.2021.9562066

Cited by 27 publications

(8 citation statements)

References 30 publications

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“…However, these works did not demonstrate interaction with the robot's surroundings -a prerequisite of DMM. Minniti et al in [12] showcased impressive dexterity and interaction in performing mobile manipulation tasks with their balbot manipulator, using model predictive control and an adaptive parameter estimator to open doors and lift objects. Stillman et al presented the Golem Krang [13], a bi-wheeled manipulator capable of balancing while performing heavy lifting.…”

Section: Related Workmentioning

confidence: 99%

Hands-free Telelocomotion of a Wheeled Humanoid

Purushottam¹,

Jung

Murphy

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Humanoid robots have the potential to help human workers by realizing physically demanding manipulation tasks such as moving large boxes within warehouses. We define such tasks as Dynamic Mobile Manipulation (DMM). This paper presents a framework for DMM via whole-body teleoperation, built upon three key contributions: Firstly, a teleoperation framework employing a Human Machine Interface (HMI) and a bi-wheeled humanoid, SATYRR, is proposed. Secondly, the study introduces a dynamic locomotion mapping, utilizing human-robot reduced order models, and a kinematic retargeting strategy for manipulation tasks. Additionally, the paper discusses the role of whole-body haptic feedback for wheeled humanoid control. Finally, the system's effectiveness and mappings for DMM are validated through locomanipulation experiments and heavy box pushing tasks. Here we show two forms of DMM: grasping a target moving at an average speed of 0.4 m/s, and pushing boxes weighing up to 105% of the robot's weight. By simultaneously adjusting their pitch and using their arms, the pilot adjusts the robot pose to apply larger contact forces and move a heavy box at a constant velocity of 0.2 m/s.

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Section: Related Workmentioning

confidence: 99%

Hands-free Telelocomotion of a Wheeled Humanoid

Purushottam¹,

Jung

Murphy

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Optimal Control approaches have demonstrated promising results on complex manipulation tasks that require conjoint movements of arm and base. Model predictive control (MPC) based approaches have demonstrated strong performance on whole-body control tasks such as door opening [13], [29], obstacle avoidance [12], and articulated objects [30]. Constraints are explicitly incorporated into the objective function and optimized over a (usually fixed) rollout horizon.…”

Section: Related Workmentioning

confidence: 99%

“…Planning approaches [9]- [11] come with asymptotic optimality guarantees, but scale unfavorably with the size of the configuration space, can have long planning times, and often require frequent re-planning in dynamic environments. Model predictive control (MPC) formulations explicitly define and optimize over a range of collision and desirability constraints, and recently achieved strong results in mobile manipulation [12], [13]. However, they are computationally expensive, often do not optimize past a limited horizon, and can struggle when objectives oppose each other.…”

Section: Introductionmentioning

confidence: 99%

N$^2$M$^2$: Learning Navigation for Arbitrary Mobile Manipulation Motions in Unseen and Dynamic Environments

Honerkamp¹,

Welschehold²,

Valada³

2022

Preprint

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Despite its importance in both industrial and service robotics, mobile manipulation remains a significant challenge as it requires a seamless integration of end-effector trajectory generation with navigation skills as well as reasoning over long-horizons. Existing methods struggle to control the large configuration space, and to navigate dynamic and unknown environments. In previous work, we proposed to decompose mobile manipulation tasks into a simplified motion generator for the end-effector in task space and a trained reinforcement learning agent for the mobile base to account for kinematic feasibility of the motion. In this work, we introduce Neural Navigation for Mobile Manipulation (N 2 M 2 ) which extends this decomposition to complex obstacle environments and enables it to tackle a broad range of tasks in real world settings. The resulting approach can perform unseen, long-horizon tasks in unexplored environments while instantly reacting to dynamic obstacles and environmental changes. At the same time, it provides a simple way to define new mobile manipulation tasks. We demonstrate the capabilities of our proposed approach in extensive simulation and real-world experiments on multiple kinematically diverse mobile manipulators. Code and videos are publicly available at http://mobile-rl.cs.uni-freiburg.de.

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“…Over the years, model predictive control (MPC) has steadily emerged as a highly effective control technique widely utilized in various disciplines, with applications spanning robotics [1,2], aerospace [3], and process control [4]. This surge in MPC's utility is attributable to its inherent capacity to provide accurate state predictions and subsequent control actions, given a robust and accurate dynamical model.…”

Section: Introductionmentioning

confidence: 99%

Continuous adaptive nonlinear model predictive control using spiking neural networks and real-time learning

Halaly,

Tsur

2024

Neuromorph. Comput. Eng.

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Model Predictive Control (MPC) is a prominent control paradigm providing accurate state prediction and subsequent control actions for intricate dynamical systems with applications ranging from autonomous driving to star tracking. However, there is an apparent discrepancy between the model’s mathematical description and its behavior in real-world conditions, affecting its performance in real-time. In this work, we propose a novel neuromorphic spiking neural network for continuous adaptive non-linear MPC. By using real-time learning, our design significantly reduces dynamic error and augments model accuracy, while simultaneously addressing unforeseen situations. We evaluated our framework using real-world scenarios in autonomous driving, implemented in a physics-driven simulation. We tested our design with various vehicles (from a Tesla Model 3 to an Ambulance) experiencing malfunctioning and swift steering scenarios. We demonstrate significant improvements in dynamic error rate compared with traditional MPC implementation with up to 89.87% median prediction error reduction with 5 spiking neurons and up to 96.95% with 5000 neurons. Our results may pave the way for novel applications in real-time control and stimulate further studies in the adaptive control realm with spiking neural networks.

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Model Predictive Robot-Environment Interaction Control for Mobile Manipulation Tasks

Cited by 27 publications

References 30 publications

Hands-free Telelocomotion of a Wheeled Humanoid

Hands-free Telelocomotion of a Wheeled Humanoid

N$^2$M$^2$: Learning Navigation for Arbitrary Mobile Manipulation Motions in Unseen and Dynamic Environments

Continuous adaptive nonlinear model predictive control using spiking neural networks and real-time learning

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