Design of a Momentum-Based Control Framework and Application to the Humanoid Robot Atlas

Koolen, Twan; Bertrand, Sylvain; Thomas, Gray C.; Boer, Tomas de; Wu, Tingfan; Smith, Jesper; Englsberger, Johannes; Pratt, Jerry

doi:10.1142/s0219843616500079

Cited by 231 publications

(206 citation statements)

References 10 publications

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“…Xinjilefu et al [5] directly estimated this offset using an inverted pendulum model to infer modeling error and/or unexpected external forces. Instead, the approach of Koolen et al [6] modeled the elasticity of their robot's leg joints to better distribute error. Our own prior work [7] utilized that elasticity model within a EKF filter to achieve low drift proprioceptive state estimation with the Boston Dynamics Atlas robot.…”

Section: Related Work a Localization Of Humanoid Robotsmentioning

confidence: 99%

“…These measurements were integrated into our state estimate using a Gaussian particle filter at the 40Hz frame rate of the LIDAR. Koolen et al [6] described their approach which instead used lower frequency ICP registration of full 3D point clouds.…”

Section: Related Work a Localization Of Humanoid Robotsmentioning

confidence: 99%

“…Firstly, a kinematic-inertial state estimator is used within the closed-loop locomotion controller (either [6] or [7]) and computes a stable but drifting estimate of the robot's pelvis pose at high frequency (∼ 500Hz). Typical estimation drift is presented in Figure 9 for the continuous walking experiment.…”

Section: System Overview and Requirementsmentioning

confidence: 99%

“…We leverage the drifting state estimate derived from our humanoid state estimator [6]- [7] and develop a registration strategy based on careful pre-filtering, adjustment to overlap variation and non-incremental alignment. The proposed approach increases the basin of attraction of the error minimization step of ICP, allowing us to align to a single reference cloud.…”

Section: Localization During the Drc Finalsmentioning

confidence: 99%

See 3 more Smart Citations

Overlap-based ICP tuning for robust localization of a humanoid robot

Nobili

Scona

Caravagna

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-State estimation techniques for humanoid robots are typically based on proprioceptive sensing and accumulate drift over time. This drift can be corrected using exteroceptive sensors such as laser scanners via a scene registration procedure. For this procedure the common assumption of high point cloud overlap is violated when the scenario and the robot's point-of-view are not static and the sensor's field-of-view (FOV) is limited. In this paper we focus on the localization of a robot with limited FOV in a semi-structured environment. We analyze the effect of overlap variations on registration performance and demonstrate that where overlap varies, outlier filtering needs to be tuned accordingly. We define a novel parameter which gives a measure of this overlap. In this context, we propose a strategy for robust non-incremental registration. The pre-filtering module selects planar macro-features from the input clouds, discarding clutter. Outlier filtering is automatically tuned at run-time to allow registration to a common reference in conditions of non-uniform overlap. An extensive experimental demonstration is presented which characterizes the performance of the algorithm using two humanoids: the NASA Valkyrie, in a laboratory environment, and the Boston Dynamics Atlas, during the DARPA Robotics Challenge Finals.

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Section: Related Work a Localization Of Humanoid Robotsmentioning

confidence: 99%

Section: Related Work a Localization Of Humanoid Robotsmentioning

confidence: 99%

Section: System Overview and Requirementsmentioning

confidence: 99%

Section: Localization During the Drc Finalsmentioning

confidence: 99%

See 2 more Smart Citations

Overlap-based ICP tuning for robust localization of a humanoid robot

Nobili

Scona

Caravagna

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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“…Centroidal-dynamics based approaches have been highly effective at generating strategies which transfer to full, high-dimensional robots (e.g. [10,20,8,14]), motivating their use here as well. Stephens [33] characterized and studied the LIPM in the context of push recovery.…”

Section: Introductionmentioning

confidence: 99%

Balancing and Step Recovery Capturability via Sums-of-Squares Optimization

Posa

Koolen

Tedrake

2017

Robotics: Science and Systems XIII

Self Cite

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Abstract-A fundamental requirement for legged robots is to maintain balance and prevent potentially damaging falls whenever possible. As a response to outside disturbances, fall prevention can be achieved by a combination of active balancing actions, e.g. through ankle torques and upper-body motion, and through reactive step placement. While it is widely accepted that stepping is required to respond to large disturbances, the limits of active motions on balancing and step recovery are only well understood for the simplest of walking models. Recent advances in convex optimization-based verification and control techniques enable a more complete understanding of the limits and capabilities of more complex models. In this work, we present an algorithmic approach for formal analysis of the viable-capture basins of walking robots, calculating both inner and outer approximations and corresponding push recovery control strategies. Extending beyond the classic Linear Inverted Pendulum Model (LIPM), we analyze a series of centroidal momentum based planar walking models, examining the effects of center of mass height, angular momentum, and impact dynamics during stepping on capturability. This formal analysis enables an explicit calculation of the differences between these models, and assessment of whether the simplest models ultimately sacrifice capability, and thus stability, when designing push recovery control policies.

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Memristor‐enhanced humanoid robot control system – Part I: Theory behind the novel memcomputing paradigm

Ascoli

Baumann

Tetzlaff

et al. 2017

Circuit Theory & Apps

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SUMMARYMyon is a humanoid robot where each joint is controlled independently by a supervised bio-inspired artificial neural network inducing the correction of a number of distinct actions depending on the excitation. One of the control strategies, which the network, located within a certain joint, may implement, allows a controlled motion of the limb connected to the joint from a stable state up to a prescribed height and the maintenance of the new position afterwards. The original approach adopted for this control operation is stable and robust but results in slow and energy-inefficient limb movements. This work proposes a novel, low-power, timeefficient and adaptive memristor-centred control strategy for the aforementioned robot action. The idea is based upon the exploitation of the combined ability of memristors to store and process data in the same physical location. The part I paper sets the theoretic foundations for the memcomputing paradigm to robot motion control, while the part II manuscript shall demonstrate its benefits over the original approach in terms of energy, and speed, and the inheritance from the standard strategy of a good level of adaptability to changes in the limb load on the basis of the analysis of circuit-theoretic models adopting an ideal and a real memristor, respectively.

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Design of a Momentum-Based Control Framework and Application to the Humanoid Robot Atlas

Cited by 231 publications

References 10 publications

Overlap-based ICP tuning for robust localization of a humanoid robot

Overlap-based ICP tuning for robust localization of a humanoid robot

Balancing and Step Recovery Capturability via Sums-of-Squares Optimization

Memristor‐enhanced humanoid robot control system – Part I: Theory behind the novel memcomputing paradigm

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