Learning When to Trust a Dynamics Model for Planning in Reduced State Spaces

McConachie, Dale; Power, Thomas; Mitrano, Peter; Berenson, Dmitry

doi:10.1109/lra.2020.2972858

Cited by 28 publications

(20 citation statements)

References 26 publications

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“…Especially for the learned models, we can never hope to collect enough data to produce an accurate model in the entire state space (which is infinite dimensional). Thus [29] and [30] have developed methods to reason about the validity of a (learned) model for a given state and action and used these methods to reason about model uncertainty in planning and control.…”

Section: Sensingmentioning

confidence: 99%

Challenges and Outlook in Robotic Manipulation of Deformable Objects

Zhu

Cherubini

Dune

et al. 2022

IEEE Robot. Automat. Mag.

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123

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Deformable object manipulation (DOM) is an emerging research problem in robotics. The ability to manipulate deformable objects endows robots with higher autonomy and promises new applications in the industrial, services, and healthcare sectors. However, compared to rigid object manipulation, the manipulation of deformable objects is considerably more complex, and is still an open research problem. Tackling the challenges in DOM demands breakthroughs in almost all aspects of robotics, namely hardware design, sensing, deformation modeling, planning, and control. In this article, we highlight the main challenges that arise by considering deformation and review recent advances in each sub-field. A particular focus of our paper lies in the discussions of these challenges and proposing promising directions of research.

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Section: Sensingmentioning

confidence: 99%

Challenges and Outlook in Robotic Manipulation of Deformable Objects

Zhu

Cherubini

Dune

et al. 2022

IEEE Robot. Automat. Mag.

Self Cite

123

View full text Add to dashboard Cite

show abstract

“…Other work [20] has used a set of simple models and a selection mechanism to choose between them. [8] use a given simplified dynamics model and learns a classifier on whether a given transition is reliable. We use GP uncertainty to model transition reliability rather than a classifier.…”

Section: Related Workmentioning

confidence: 99%

“…We can exploit this subset to perform tasks such as bringing the mass to a target, even without a globally-accurate dynamics model. Simple models are often used in this way, for example in deformable object manipulation [7], [8] and control for humanoids [9].…”

Section: Introductionmentioning

confidence: 99%

Keep it Simple: Data-efficient Learning for Controlling Complex Systems with Simple Models

Power¹,

Berenson²

2021

Preprint

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When manipulating a novel object with complex dynamics, a state representation is not always available, for example for deformable objects. Learning both a representation and dynamics from observations requires large amounts of data. We propose Learned Visual Similarity Predictive Control (LVSPC), a novel method for data-efficient learning to control systems with complex dynamics and high-dimensional state spaces from images. LVSPC leverages a given simple model approximation from which image observations can be generated. We use these images to train a perception model that estimates the simple model state from observations of the complex system online. We then use data from the complex system to fit the parameters of the simple model and learn where this model is inaccurate, also online. Finally, we use Model Predictive Control and bias the controller away from regions where the simple model is inaccurate and thus where the controller is less reliable. We evaluate LVSPC on two tasks; manipulating a tethered mass and a rope. We find that our method performs comparably to state-of-the-art reinforcement learning methods with an order of magnitude less data. LVSPC also completes the rope manipulation task on a real robot with 80% success rate after only 10 trials, despite using a perception system trained only on images from simulation.

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“…For problems in grasping soft objects, modeling and simulation of deformable objects have recently been considerable interest in many robotic applications [2], [3], to manipulating soft tissue [4], [5], cloth [6], [7], and even fluids [8], [9]. Among soft structures, deformable linear objects (DLOs), including rope-like objects, strings, cables, beams, etc., are studied (cable routing [10], wire insertion [11], flexible rope [12], and knotting of surgical thread [13]). Recently, techniques involving visual servoing [14], [15], latent dynamics learning [16], and adaptive estimation [17], have all be explored for controlling DLOs.…”

Section: Introductionmentioning

confidence: 99%

Differentiable Robotic Manipulation of Deformable Rope-like Objects Using Compliant Position-based Dynamics

Liu¹,

Entong²,

Lu³

et al. 2022

Preprint

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Robot manipulation of rope-like objects is an interesting problem that has some critical applications, such as autonomous robotic suturing. Solving for and controlling rope is difficult due to the complexity of rope physics and the challenge of building fast and accurate models of deformable materials. While more data-driven approaches have become more popular for finding controllers that learn to do a single task, there is still a strong motivation for a modelbased method that could be used to solve a large variety of optimization problems. Towards this end, we introduced compliant, position-based dynamics (XPBD) to model rope-like objects. Using geometric constraints, the model can represent the coupling of shear/stretch and bend/twist effects. Of crucial importance is that our formulation is differentiable, which can solve parameter estimation problems and improve the matching of rope physics to real-life scenarios (i.e., the realto-sim problem). For the generality of rope-like objects, two different solvers are proposed to handle the inextensible and extensible effects of varied material stiffness for the rope. We demonstrate our framework's robustness and accuracy on realto-sim experimental setups using the Baxter robot and the da Vinci research kit (DVRK) [1]. Our work leads to a new path for robotic manipulation of the deformable rope-like object taking advantage of the ready-to-use gradients.

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Learning When to Trust a Dynamics Model for Planning in Reduced State Spaces

Cited by 28 publications

References 26 publications

Challenges and Outlook in Robotic Manipulation of Deformable Objects

Challenges and Outlook in Robotic Manipulation of Deformable Objects

Keep it Simple: Data-efficient Learning for Controlling Complex Systems with Simple Models

Differentiable Robotic Manipulation of Deformable Rope-like Objects Using Compliant Position-based Dynamics

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