Compact models of human reaching motions for robotic control in everyday manipulation tasks

Stulp, Freek; Kresse, Ingo; Maldonado, Alexis; Ruiz, Federico; Fedrizzi, Andreas; Beetz, Michael

doi:10.1109/devlrn.2009.5175511

Cited by 9 publications

(3 citation statements)

References 22 publications

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“…In contrast, robotics research has often addressed the task of generating collision free trajectories (e.g, Kavraki and LaValle (2008); LaValle and Kuffner (2001); Lozano-Perez (1987)), generating reaching motions in free space (e.g, Hersch and Billard (2006); Metta et al (2011); Stulp et al (2009)), and manipulating objects in uncluttered environments (e.g, Hsiao et al (2010); Jain and Kemp (2010a); Natale and Torres-Jara (2006); Pastor et al (2011);Romano et al (2011);Saxena et al (2008)).…”

Section: A Manipulation In Cluttermentioning

confidence: 99%

Reaching in clutter with whole-arm tactile sensing

Jain

Killpack

Edsinger³

et al. 2013

The International Journal of Robotics Research

103

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We begin this paper by presenting our approach to robot manipulation, which emphasizes the benefits of making contact with the world across the entire manipulator. We assume that low contact forces are benign, and focus on the development of robots that can control their contact forces during goaldirected motion. Inspired by biology, we assume that the robot has low-stiffness actuation at its joints, and tactile sensing across the entire surface of its manipulator. We then describe a novel controller that exploits these assumptions. The controller only requires haptic sensing and does not need an explicit model of the environment prior to contact. It also handles multiple contacts across the surface of the manipulator. The controller uses model predictive control (MPC) with a time horizon of length one, and a linear quasi-static mechanical model that it constructs at each time step. We show that this controller enables both real and simulated robots to reach goal locations in high clutter with low contact forces. Our experiments include tests using a real robot with a novel tactile sensor array on its forearm reaching into simulated foliage and a cinder block. In our experiments, robots made contact across their entire arms while pushing aside movable objects, deforming compliant objects, and perceiving the world.

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Section: A Manipulation In Cluttermentioning

confidence: 99%

Reaching in clutter with whole-arm tactile sensing

Jain

Killpack

Edsinger³

et al. 2013

The International Journal of Robotics Research

103

View full text Add to dashboard Cite

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“…This significantly lower-dimensional representation allows to distinguish different kinds of reaching trajectories, or to select trajectories for a robot to imitate. We are currently investigating the integration of the compact models for hand trajectories described in [9].…”

Section: Continuous Motion Modelsmentioning

confidence: 99%

Towards automated models of activities of daily life

Beetz

Tenorth

Jain

et al. 2010

TAD

Self Cite

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We propose automated probabilistic models of everyday activities (AM-EvA) as a novel technical means for the perception, interpretation, and analysis of everyday manipulation tasks and activities of daily life. AM-EvAs are detailed, comprehensive models describing human actions at various levels of abstraction from raw poses and trajectories to motions, actions and activities. They integrate several kinds of action models in a common, knowledge-based framework to combine observations of human activities with a-priori knowledge about actions. AM-EvAs enable robots and technical systems to analyze actions in the complete situation and activity context. They make the classification and assessment of actions and situations objective and can justify the probabilistic interpretation with respect to the activities the concepts have been learned from. AM-EvAs allow to analyze and compare the way humans perform actions which can help with autonomy assessment and diagnosis. We describe in this paper the concept and implementation of the AM-EvA system and show example results from the observation and analysis of table-setting episodes.

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“…Primitive are often used as building blocks to higher level behaviors, such as words being composed of syllables [85], [104]. Primitives can also be used to make operation simpler and are a step toward using triplet-based world models 1 [105], [106].…”

Section: Sensorimotor Controlmentioning

confidence: 99%

Biomimetic learning, not learning biomimetics: A survey of developmental learning

Jennings

Ordóñez

2010

Proceedings of the IEEE 2010 National Aerospace &Amp; Electronics Conference

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Rather than try to learn skills directly, developmental learning seeks to create a general purpose system inspired by the human developmental process. The premise is that intelligent behavior can only be achieved through the course of experience, and attempts to circumvent this approach will lead to a plateau limiting performance. There are many facets of this approach and some of the common examples are categorization, sensorimotor control and social learning. These are relatively simple tasks for people, yet challenging for an autonomous robot. A survey of research in this recent field is presented. The concepts and the tools used are described so that other disciplines may have a understanding of this field and use the paradigm in their applications.

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Compact models of human reaching motions for robotic control in everyday manipulation tasks

Cited by 9 publications

References 22 publications

Reaching in clutter with whole-arm tactile sensing

Reaching in clutter with whole-arm tactile sensing

Towards automated models of activities of daily life

Biomimetic learning, not learning biomimetics: A survey of developmental learning

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