Operational Space Control: A Theoretical and Empirical Comparison

Nakanishi, Jun; Cory, Rick; Mistry, Michael; Peters, Jan; Schaal, Stefan

doi:10.1177/0278364908091463

Cited by 342 publications

(277 citation statements)

References 40 publications

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“…Task-space motion control, initially developed by Liegeois [4], is a wellestablished concept in robotics (see [6] for a recent survey). Common to taskspace control methods is to transfer motion specified in a space relevant to a task to joint-space motion.…”

Section: Compliant Control For Service Robotsmentioning

confidence: 99%

Perception of Deformable Objects and Compliant Manipulation for Service Robots

Stückler

Behnke

2015

Soft Robotics

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We identified softness in robot control as well as robot perception as key enabling technologies for future service robots. Compliance in motion control compensates for small errors in model acquisition and estimation and enables safe physical interaction with humans. The perception of shape similarities and deformations allows a robot to adapt its skills to the object at hand, given a description of the skill that generalizes between different objects. In this paper, we present our approaches to compliant control and object manipulation skill transfer for service robots. We report on evaluation results and public demonstrations of our approaches.

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Section: Compliant Control For Service Robotsmentioning

confidence: 99%

Perception of Deformable Objects and Compliant Manipulation for Service Robots

Stückler

Behnke

2015

Soft Robotics

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“…Tracking performance of the controllers is evaluated on a 'star-like' asterisk pattern [7] as seen in Figure 2. This trajectory is obtained by first moving in a straight line outwards from a centre point, then retracing back inwards to the centre, repeating this pattern in eight different directions in a sequential manner.…”

Section: Simulationsmentioning

confidence: 99%

“…However, such methods are still reliant upon adequate knowledge of the structure of the dynamic model and are thus susceptible to modeling errors and changes in the model structure. Furthermore, accurate estimation of the model parameters requires sufficiently rich data sets [2], [6], and even when available, may result in physically inconsistent parameters meaning that constraints must be applied to the identified parameters [2], [7].…”

Section: Introductionmentioning

confidence: 99%

Online Incremental Learning of Inverse Dynamics Incorporating Prior Knowledge

Cruz

Kulić

Owen

2011

Lecture Notes in Computer Science

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Abstract. Recent approaches to model-based manipulator control involve data-driven learning of the inverse dynamics relationship of a manipulator, eliminating the need for any knowledge of the system model. Ideally, such algorithms should be able to process large amounts of data in an online and incremental manner, thus allowing the system to adapt to changes in its model structure or parameters. Locally Weighted Projection Regression (LWPR) and other non-parametric regression techniques have been applied to learn manipulator inverse dynamics. However, a common issue amongst these learning algorithms is that the system is unable to generalize well outside of regions where it has been trained. Furthermore, learning commences entirely from 'scratch,' making no use of any a-priori knowledge which may be available. In this paper, an online, incremental learning algorithm incorporating prior knowledge is proposed. Prior knowledge is incorporated into the LWPR framework by initializing the local linear models with a first order approximation of the available prior information. It is shown that the proposed approach allows the system to operate well even without any initial training data, and further improves performance with additional online training.

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“…Practical application of floating-base inverse dynamics has been hindered by the following issues: (a) Dependence on precise dynamics models, amplified by numerical problems that can arise due to matrix inversions, in particular the inversion of the rigid body dynamics (RBD) inertia matrix [6], (b) external forces need to be measured, and (c) no general analytically correct framework for floating-base systems for arbitrary constraints from the environment and closed loop kinematic chains.…”

Section: Inverse Dynamics Controlmentioning

confidence: 99%

Compliant quadruped locomotion over rough terrain

Buchli

Kalakrishnan

Mistry

et al. 2009

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

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144

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Abstract-Many critical elements for statically stable walking for legged robots have been known for a long time, including stability criteria based on support polygons, good foothold selection, recovery strategies to name a few. All these criteria have to be accounted for in the planning as well as the control phase. Most legged robots usually employ high gain position control, which means that it is crucially important that the planned reference trajectories are a good match for the actual terrain, and that tracking is accurate. Such an approach leads to conservative controllers, i.e. relatively low speed, ground speed matching, etc. Not surprisingly such controllers are not very robust -they are not suited for the real world use outside of the laboratory where the knowledge of the world is limited and error prone. Thus, to achieve robust robotic locomotion in the archetypical domain of legged systems, namely complex rough terrain, where the size of the obstacles are in the order of leg length, additional elements are required. A possible solution to improve the robustness of legged locomotion is to maximize the compliance of the controller. While compliance is trivially achieved by reduced feedback gains, for terrain requiring precise foot placement (e.g. climbing rocks, walking over pegs or cracks) compliance cannot be introduced at the cost of inferior tracking. Thus, model-based control and -in contrast to passive dynamic walkers -active balance control is required. To achieve these objectives, in this paper we add two crucial elements to legged locomotion, i.e., floating-base inverse dynamics control and predictive force control, and we show that these elements increase robustness in face of unknown and unanticipated perturbations (e.g. obstacles). Furthermore, we introduce a novel line-based COG trajectory planner, which yields a simpler algorithm than traditional polygon based methods and creates the appropriate input to our control system. We show results from both simulation and real world of a robotic dog walking over non-perceived obstacles and rocky terrain. The results prove the effectivity of the inverse dynamics/force controller. The presented results show that we have all elements needed for robust all-terrain locomotion, which should also generalize to other legged systems, e.g., humanoid robots.

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Operational Space Control: A Theoretical and Empirical Comparison

Abstract: Dexterous manipulation with a highly redundant movement system is one of the hallmarks of human motor skills.

Cited by 342 publications

References 40 publications

Perception of Deformable Objects and Compliant Manipulation for Service Robots

Perception of Deformable Objects and Compliant Manipulation for Service Robots

Online Incremental Learning of Inverse Dynamics Incorporating Prior Knowledge

Compliant quadruped locomotion over rough terrain

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