Tactile Dexterity: Manipulation Primitives with Tactile Feedback

Hogan, Francois R.; Ballester, José; Dong, Siyuan; Rodríguez, Alberto

doi:10.48550/arxiv.2002.03236

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…Much work regarding vision-based robotic grasping and manipulation for parallel jaw grippers mounted on a single robot arm has been reported in the surveys by [ 8 , 19 , 20 ]. Such vision-based approaches present fundamental limitations when it comes to dexterous manipulation tasks requiring more accurate and controlled contact interactions, such as object reorientation, object insertion or almost any kind of object use [ 21 ]. Many practical applications, especially in home settings, would require robots to have semi-precise or gentle placement of components, which would require contact information about the object pose and haptic feedback during manipulation.…”

Section: Approaches To Dexterous Robotic Manipulationmentioning

confidence: 99%

Towards Haptic-Based Dual-Arm Manipulation

Turlapati

Campolo

2022

Sensors

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Vision is the main component of current robotics systems that is used for manipulating objects. However, solely relying on vision for hand−object pose tracking faces challenges such as occlusions and objects moving out of view during robotic manipulation. In this work, we show that object kinematics can be inferred from local haptic feedback at the robot−object contact points, combined with robot kinematics information given an initial vision estimate of the object pose. A planar, dual-arm, teleoperated robotic setup was built to manipulate an object with hands shaped like circular discs. The robot hands were built with rubber cladding to allow for rolling contact without slipping. During stable grasping by the dual arm robot, under quasi-static conditions, the surface of the robot hand and object at the contact interface is defined by local geometric constraints. This allows one to define a relation between object orientation and robot hand orientation. With rolling contact, the displacement of the contact point on the object surface and the hand surface must be equal and opposite. This information, coupled with robot kinematics, allows one to compute the displacement of the object from its initial location. The mathematical formulation of the geometric constraints between robot hand and object is detailed. This is followed by the methodology in acquiring data from experiments to compute object kinematics. The sensors used in the experiments, along with calibration procedures, are presented before computing the object kinematics from recorded haptic feedback. Results comparing object kinematics obtained purely from vision and from haptics are presented to validate our method, along with the future ideas for perception via haptic manipulation.

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Section: Approaches To Dexterous Robotic Manipulationmentioning

confidence: 99%

Towards Haptic-Based Dual-Arm Manipulation

Turlapati

Campolo

2022

Sensors

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“…Contact geometry has been applied for pose estimation [1,3], material perception [15], grasp adjustment [16,17], contour following [18,19], and ball manipulation [20,12]. Researchers also combine contact geometry with forces for slip detection [21], hardness measurement [22], cube manipulation [23], and cable manipulation [24].…”

Section: Related Workmentioning

confidence: 99%

GelSight Wedge: Measuring High-Resolution 3D Contact Geometry with a Compact Robot Finger

Wang¹,

She²,

Romero³

et al. 2021

Preprint

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Vision-based tactile sensors have the potential to provide important contact geometry to localize the objective with visual occlusion. However, it is challenging to measure highresolution 3D contact geometry for a compact robot finger, to simultaneously meet optical and mechanical constraints. In this work, we present the GelSight Wedge sensor, which is optimized to have a compact shape for robot fingers, while achieving highresolution 3D reconstruction. We evaluate the 3D reconstruction under different lighting configurations, and extend the method from 3 lights to 1 or 2 lights. We demonstrate the flexibility of the design by shrinking the sensor to the size of a human finger for fine manipulation tasks. We also show the effectiveness and potential of the reconstructed 3D geometry for pose tracking in the 3D space.

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Dynamic primitives in constrained action: systematic changes in the zero-force trajectory

Hermus,

Doeringer,

Sternad

et al. 2024

Journal of Neurophysiology

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Humans substantially outperform robotic systems in tasks that require physical interaction, despite seemingly inferior muscle bandwidth and slow neural information transmission. The control strategies that enable this performance remain poorly understood. To bridge this gap, this study examined kinematically-constrained motion as an intermediate step between the widely-studied unconstrained motions and sparsely-studied physical interactions. Subjects turned a horizontal planar crank in two directions (clockwise and counterclockwise) at three constant target speeds. With the hand constrained to move in a circle, non-zero forces against the constraint were measured. This experiment exposed two observations that could not result from mechanics alone, but may be attributed to neural control composed of dynamic primitives. A plausible mathematical model of interactive dynamics (mechanical impedance) was assumed and used to 'subtract' peripheral neuromechanics. This method revealed a summary of the underlying neural control in terms of motion-a zero-force trajectory. The estimated zero-force trajectories were approximately elliptical and their orientation differed significantly with turning direction. That is consistent with control using oscillations to generate an elliptical zero-force trajectory. However, for periods longer than 2-5 seconds, motion can no longer be perceived or executed as periodic. Instead, it decomposes into a sequence of submovements, manifest as increased variability. These quantifiable performance limitations support the hypothesis that humans simplify this constrained-motion task by exploiting at least three primitive dynamic actions: oscillations, submovements, and mechanical impedance.

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Tactile Dexterity: Manipulation Primitives with Tactile Feedback

Cited by 3 publications

References 30 publications

Towards Haptic-Based Dual-Arm Manipulation

Towards Haptic-Based Dual-Arm Manipulation

GelSight Wedge: Measuring High-Resolution 3D Contact Geometry with a Compact Robot Finger

Dynamic primitives in constrained action: systematic changes in the zero-force trajectory

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