Balance control using center of mass height variation: Limitations imposed by unilateral contact

Koolen, Twan; Posa, Michael; Tedrake, Russ

doi:10.1109/humanoids.2016.7803247

Cited by 73 publications

(70 citation statements)

References 19 publications

(47 reference statements)

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“…There are also works performing capturability analysis for the more complex variable-height inverted pendulum (VHIP) model to account for the height changes of the CoM. [29][30][31] address the balance control of humanoid robot using VHIP model for planar motions. [32] further extends it to consider 3D movements, and develops an analytical tool to determine capturability in the VHIP model.…”

Section: Related Workmentioning

confidence: 99%

Robust Humanoid Contact Planning With Learned Zero- and One-Step Capturability Prediction

Lin

Righetti

Berenson

2020

IEEE Robot. Autom. Lett.

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Humanoid robots maintain balance and navigate by controlling the contact wrenches applied to the environment. While it is possible to plan dynamically-feasible motion that applies appropriate wrenches using existing methods, a humanoid may also be affected by external disturbances. Existing systems typically rely on controllers to reactively recover from disturbances. However, such controllers may fail when the robot cannot reach contacts capable of rejecting a given disturbance. In this paper, we propose a search-based footstep planner which aims to maximize the probability of the robot successfully reaching the goal without falling under disturbances. The planner considers not only the poses of the planned contact sequence, but also alternative contacts near the planned contact sequence that can be used to recover from external disturbances. Although this additional consideration significantly increases the computation load, we train neural networks to efficiently predict multi-contact zero-step and one-step capturability, which allows the planner to generate robust contact sequences efficiently. Our results show that our approach generates footstep sequences that are more robust to external disturbances than a conventional footstep planner in four challenging scenarios.

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Section: Related Workmentioning

confidence: 99%

Robust Humanoid Contact Planning With Learned Zero- and One-Step Capturability Prediction

Lin

Righetti

Berenson

2020

IEEE Robot. Autom. Lett.

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“…This approach uses semi-definite programming to identify the limits of safety in the state space of a system as well as associated controllers for a broad class of nonlinear [5], [6], [7] and hybrid systems [8], [9]. These safe sets can take the form of reachable sets (sets that can reach a known safe state) [10], [9], [5] or invariant sets (sets whose members can be controlled to remain in the set indefinitely) in state space [11], [8], [12]. However, the representation of each of these sets in state space severely restricts the size of the problem that can be tackled by these approaches.…”

Section: Introductionmentioning

confidence: 99%

Walking With Confidence: Safety Regulation for Full Order Biped Models

Smit-Anseeuw

Remy

Vasudevan

2019

IEEE Robot. Autom. Lett.

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Safety guarantees are valuable in the control of walking robots, as falling can be both dangerous and costly. Unfortunately, set-based tools for generating safety guarantees (such as sums-of-squares optimization) are typically restricted to simplified, low-dimensional models of walking robots. For more complex models, methods based on hybrid zero dynamics can ensure the local stability of a pre-specified limit cycle, but provide limited guarantees. This paper combines the benefits of both approaches by using sums-of-squares optimization on a hybrid zero dynamics manifold to generate a guaranteed safe set for a 10-dimensional walking robot model. Along with this set, this paper describes how to generate a controller that maintains safety by modifying the manifold parameters when on the edge of the safe set. The proposed approach, which is applied to a bipedal Rabbit model, provides a roadmap for applying sums-of-squares techniques to high dimensional systems. This opens the door for a broad set of tools that can generate flexible and safe controllers for complex walking robot models.

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“…Additionally, we enforce the physical constraint that the variation in height satisfy |z cm −z cm | ≤ z max , defining a corresponding unsafe region. As with the LIPM, this is a zero impact model, and so the reset dynamics remain unchanged from (15), with the addition that z cm+ = z cm− andż cm+ =ż cm− . Fig.…”

Section: B Variable Heightmentioning

confidence: 99%

“…4 illustrates a slice of the the viable capture-regions for this model, where z cm =z cm andż cm = 0, demonstrating the marginal improvement in control authority gained by this additional control authority. Koolen et al [15] also analyzed a variable height model, for balancing only, although there are a few key differences between that work and this. The approach there was able to exactly calculate the 0-step viablecapture basin, without approximation.…”

Section: B Variable Heightmentioning

confidence: 99%

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

Posa

Koolen

Tedrake

2017

Robotics: Science and Systems XIII

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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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Balance control using center of mass height variation: Limitations imposed by unilateral contact

Cited by 73 publications

References 19 publications

Robust Humanoid Contact Planning With Learned Zero- and One-Step Capturability Prediction

Robust Humanoid Contact Planning With Learned Zero- and One-Step Capturability Prediction

Walking With Confidence: Safety Regulation for Full Order Biped Models

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

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