Motion Synthesis, Learning and Abstraction through Parameterized Smooth Map from Sensors to Behaviors

Nakamura, Yoshihiko; Yamazaki, Tomotaka; Mizushima, Nagamasa

doi:10.1007/978-1-4471-1580-9_7

Cited by 15 publications

(2 citation statements)

References 3 publications

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“…The "teaching by demonstration" approach to machine hands programming applies more generally to systems that do not just mimics a human hand motion, but learn from a sequence of exemplary manipulative operations of the human hand the "skill" that is employed to solve different tasks. This research avenue is currently attracting much attention, as witnessed by the growing literature (see, e.g., [40], [8], [80], [81], [22], [146], [123], [60], [189], and [49]). In some cases, authors are using concepts developed in the robotic literature to perform analysis of the human manipulation behavior, with results that are interesting for both their fundamental psychophysical meaning (illustrating those links between hands and intelligence Anaxagoras was alluding to), and for fallouts on applications of particular social relevance, such as rehabilitation (viz., [84], [70], [175]).…”

Section: Human Operabilitymentioning

confidence: 99%

Hands for dexterous manipulation and robust grasping: a difficult road toward simplicity

Bicchi

2000

IEEE Trans. Robot. Automat.

787

385

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In this paper, an attempt at summarizing the evolution and the state of the art in the field of robot hands is made. In such exposition, a critical evaluation of what in the author's view are the leading ideas and emerging trends is privileged with respect to exhaustiveness of citations.The survey is focused mainly on three types of functional requirements a machine hand can be assigned in an artificial system, namely, manipulative dexterity, grasp robustness, and human operability. A basic distinction is made between hands designed for mimicking the human anatomy and physiology, and hands designed to meet restricted, practical requirements. In the latter domain, arguments are presented in favor of a "minimalistic" attitude in the design of hands for practical applications, i.e., use the least number of actuators, the simplest set of sensors, etc., for a given task. To achieve this rather obvious engineering goal is a challenge to our community. The paper illustrates some of the new, sometimes difficult, problems that are brought about by building and controlling simpler, more practical devices.

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Section: Human Operabilitymentioning

confidence: 99%

Hands for dexterous manipulation and robust grasping: a difficult road toward simplicity

Bicchi

2000

IEEE Trans. Robot. Automat.

787

385

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“…This type of behavior has also been used for interactive real-time guidance [13] and reflective grasping [14]. In [15], Nakamura et al demonstrate the effectiveness of coaching the mapping in between preprocessed sensor information and predefined behavior primitives on grasping different objects.…”

Section: A Motivationmentioning

confidence: 99%

Self-organizing sensory-motor map for low-level touch reactions

Mittendorfer

Cheng

2011

2011 11th IEEE-RAS International Conference on Humanoid Robots

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Abstract-In this paper, we present a new method to automatically transfer touch stimulation into controllable, reflex reactions for articulated robots -such as a humanoid robot. Our work has been motivated by the necessity to automate the reaction setup for an increasing number of our multimodal artificial sensor skin units (HEX-O-SKIN). Our method therefore evaluates the effect of isolated, sinusoidal degree of freedom (DoF) movements, around a current pose of the robot, towards the motion of each sensor unit. We do this by exploiting data from a 3-axis accelerometer -one of the multiple modalities located on every of our sensor units (SU). Direction and amplitude from all three axes enable us to generate signed weights, one per DoF and SU, leading to the partial construction of a sensory-motor map. In this paper, we focus on reactions towards lateral sensory modalitiessuch as a distance sensor. A higher acceleration along the surface normal thus leads to a higher lateral weight, while unwanted sideway movements decrease it. We then define a reaction controller at the level of each sensor unit. The sensorymotor map is used to map these local reactions, from units distributed all over the robot, into the robot's motor space. Through activation, inhibition or inversion it is possible to adapt these low-level controller instances to a given context. Here, we show experiments with a KUKA lightweight robotic arm reacting evasively or aggressively towards contact with a lateral distance sensor, emulating the sensation of light touch. In comparison to other related works, our method does not suffer from occlusion, complex touch situations or long calibration time.

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Modeling a Central Pattern Generator to Generate the Biped Locomotion of a Bipedal Robot Using Rayleigh Oscillators

Mondal

Nandy

Verma

et al. 2011

Communications in Computer and Information Science

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Motion Synthesis, Learning and Abstraction through Parameterized Smooth Map from Sensors to Behaviors

Cited by 15 publications

References 3 publications

Hands for dexterous manipulation and robust grasping: a difficult road toward simplicity

Hands for dexterous manipulation and robust grasping: a difficult road toward simplicity

Self-organizing sensory-motor map for low-level touch reactions

Modeling a Central Pattern Generator to Generate the Biped Locomotion of a Bipedal Robot Using Rayleigh Oscillators

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