Using human studies to analyze capabilities of underactuated and compliant hands in manipulation tasks

Morrow, John; Kothari, Ammar; Ong, Yi Herng; Harlan, Nathan; Balasubramanian, R.; Grimm, Cindy

doi:10.1109/iros.2018.8594344

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…In this example, we evaluate how well three commercial robot hands might work for grasping specific objects. Our three hands are: a modified Barrett (puppet version from [19]), the Kinova Jaco2, and the Robotiq 2f-85 hands. Our example tasks are as follows: 1) grasping an apple and Cheez-It box, 2) grasping a lock with the intent to lock it on a chest, and 3) grasping a hammer by its handle for use (see Fig.…”

Section: Demonstrating and Using Measurementsmentioning

confidence: 99%

Measuring a Robot Hand's Graspable Region using Power and Precision Grasps

Morrow¹,

Campbell²,

Nishat³

et al. 2022

Preprint

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The variety of robotic hand designs and actuation schemes makes it difficult to measure a hand's graspable volume. For end-users, this lack of standardized measurements makes it challenging to determine a priori if a robot hand is the right size for grasping an object. We propose a practical hand measurement standard, based on precision and power grasps, that is applicable to a wide variety of robot hand designs. The resulting measurements can be used to both determine if an object will fit in the hand and characterize the size of an object with respect to the hand. Our measurement procedure uses a functional approach, based on grasping a hypothetical cylinder, that allows the measurer choose the exact hand orientation and finger configurations that are used for the measurements. This ensures that the measurements are functionally comparable while relying on the human to determine the finger configurations that best match the intended grasp. We demonstrate using our measurement standard with three commercial robot hand designs and objects from the YCB data set.

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Section: Demonstrating and Using Measurementsmentioning

confidence: 99%

Measuring a Robot Hand's Graspable Region using Power and Precision Grasps

Morrow¹,

Campbell²,

Nishat³

et al. 2022

Preprint

Self Cite

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“…However, achieving remote real-time motion control of humanoid robotic arms is a challenging and critical research area [24]. It aims to overcome challenges such as geographical distance and time delays, ensuring that operators can control robot arms in real time from remote locations.…”

Section: Introductionmentioning

confidence: 99%

Whole-Body Teleoperation Control of Dual-Arm Robot Using Sensor Fusion

Wang,

Chen,

Dong

et al. 2023

Biomimetics

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As human–robot interaction and teleoperation technologies advance, anthropomorphic control of humanoid arms has garnered increasing attention. However, accurately translating sensor-detected arm motions to the multi-degree freedom of a humanoid robotic arm is challenging, primarily due to occlusion issues with single-sensor setups, which reduce recognition accuracy. To overcome this problem, we propose a human-like arm control strategy based on multi-sensor fusion. We defined the finger bending angle to represent finger posture and employed a depth camera to capture arm movement. Consequently, we developed an arm movement tracking system and achieved anthropomorphic control of the imitation human arm. Finally, we verified our proposed method’s effectiveness through a series of experiments, evaluating the system’s robustness and real-time performance. The experimental results show that this control strategy can control the motion of the humanoid arm stably, and maintain a high recognition accuracy in the face of complex situations such as occlusion.

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