Hypothesis-based Belief Planning for Dexterous Grasping

Zito, Claudio; Ortenzi, Valerio; Adjigble, Maxime; Kopicki, Marek; Stolkin, Rustam; Wyatt, Jeremy L.

doi:10.48550/arxiv.1903.05517

Cited by 7 publications

(7 citation statements)

References 14 publications

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“…As future extension we will also build on previous work of Kopicki and Zito [9], [17], [19], [20] who have demonstrated that a set of generative models can be efficiently learned, for a robot manipulator, in one shot such that manipulative contacts and trajectories are computed for previously unseen objects. By integrating these methods in our system, we will replace the human demonstration samples as a prior estimate of which grasps the user might attempt, and more importantly it would allow generalisation to new object shapes.…”

Section: Discussionmentioning

confidence: 99%

Automatic Detection of Myocontrol Failures Based upon Situational Context Information

Heiwolt

Zito

Nowak

et al. 2019

2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)

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Myoelectric control systems for assistive devices are still unreliable. The user's input signals can become unstable over time due to e.g. fatigue, electrode displacement, or sweat. Hence, such controllers need to be constantly updated and heavily rely on user feedback. In this paper, we present an automatic failure detection method which learns when plausible predictions become unreliable and model updates are necessary.Our key insight is to enhance the control system with a set of generative models that learn sensible behaviour for a desired task from human demonstration. We illustrate our approach on a grasping scenario in Virtual Reality, in which the user is asked to grasp a bottle on a table. From demonstration our model learns the reach-to-grasp motion from a resting position to two grasps (power grasp and tridigital grasp) and how to predict the most adequate grasp from local context, e.g. tridigital grasp on the bottle cap or around the bottleneck. By measuring the error between new grasp attempts and the model prediction, the system can effectively detect which input commands do not reflect the users intention. We evaluated our model in two cases: i) with both position and rotation information of the wrist pose, and ii) with only rotational information. Our results show that our approach detects statistically highly significant differences in error distributions with p < 0.001 between successful and failed grasp attempts in both cases.

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Section: Discussionmentioning

confidence: 99%

Automatic Detection of Myocontrol Failures Based upon Situational Context Information

Heiwolt

Zito

Nowak

et al. 2019

2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)

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“…Additionally, for novel object grasping without seeing, Murali et al ( 2020 ) first propose a localization method based on touch scanning and particle filtering and establish an initial grasp, and haptic features are learned with a conditional autoencoder, which is fed into a re-grasp model to refine the initial grasp. Zito et al ( 2019 ) apply hypothesis-based belief planning for expected contacts even though the objects are non-convex and partially observable; if unexpected contact occurs, such information could be used to refine pose distribution and trigger re-planing.…”

Section: Learning-based Manipulation Methodsmentioning

confidence: 99%

A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods

Wang

et al. 2022

Front. Neurorobot.

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Multifingered robotic hands (usually referred to as dexterous hands) are designed to achieve human-level or human-like manipulations for robots or as prostheses for the disabled. The research dates back 30 years ago, yet, there remain great challenges to effectively design and control them due to their high dimensionality of configuration, frequently switched interaction modes, and various task generalization requirements. This article aims to give a brief overview of multifingered robotic manipulation from three aspects: a) the biological results, b) the structural evolvements, and c) the learning methods, and discuss potential future directions. First, we investigate the structure and principle of hand-centered visual sensing, tactile sensing, and motor control and related behavioral results. Then, we review several typical multifingered dexterous hands from task scenarios, actuation mechanisms, and in-hand sensors points. Third, we report the recent progress of various learning-based multifingered manipulation methods, including but not limited to reinforcement learning, imitation learning, and other sub-class methods. The article concludes with open issues and our thoughts on future directions.

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“…This model and those which later built upon it [14], [15], [16], [17], [18], [19] are known as analytical models and attempt to closely replicate Newtonian mechanics with their methodology. The main drawback of such approaches is their dependence upon accurate physical parameters and difficulty in modelling friction in some circumstances, as demonstrated by the work of Kopicki and Zito [20], [21], [22], [23], [24].…”

Section: Related Workmentioning

confidence: 99%

Learning Transferable Push Manipulation Skills in Novel Contexts

Howard¹,

Zito

2021

Front. Neurorobot.

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This paper is concerned with learning transferable forward models for push manipulation that can be applying to novel contexts and how to improve the quality of prediction when critical information is available. We propose to learn a parametric internal model for push interactions that, similar for humans, enables a robot to predict the outcome of a physical interaction even in novel contexts. Given a desired push action, humans are capable to identify where to place their finger on a new object so to produce a predictable motion of the object. We achieve the same behaviour by factorising the learning into two parts. First, we learn a set of local contact models to represent the geometrical relations between the robot pusher, the object, and the environment. Then we learn a set of parametric local motion models to predict how these contacts change throughout a push. The set of contact and motion models represent our internal model. By adjusting the shapes of the distributions over the physical parameters, we modify the internal model's response. Uniform distributions yield to coarse estimates when no information is available about the novel context. We call this an unbiased predictor. A more accurate predictor can be learned for a specific environment/object pair (e.g., low friction/high mass), called a biased predictor. The effectiveness of our approach is demonstrated in a simulated environment in which a Pioneer 3-DX robot equipped with a bumper needs to predict a push outcome for an object in a novel context, and we support those results with a proof of concept on a real robot. We train on two objects (a cube and a cylinder) for a total of 24,000 pushes in various conditions, and test on six objects encompassing a variety of shapes, sizes, and physical parameters for a total of 14,400 predicted push outcomes. Our experimental results show that both biased and unbiased predictors can reliably produce predictions in line with the outcomes of a carefully tuned physics simulator.

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Hypothesis-based Belief Planning for Dexterous Grasping

Cited by 7 publications

References 14 publications

Automatic Detection of Myocontrol Failures Based upon Situational Context Information

Automatic Detection of Myocontrol Failures Based upon Situational Context Information

A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods

Learning Transferable Push Manipulation Skills in Novel Contexts

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