From Motor Learning to Interaction Learning in Robots

By Olivier Sigaud, Jan Peters. 2010

121 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

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In Control and Systems Engineering

Citations in the corpus

Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning (2011, background): “The idea is related to [24], where planning is performed with fully known dynamics and a piecewise linear controller.”
On-line regression algorithms for learning mechanical models of robots: A survey (2011, background): “A major trend in robotics research is a shift from industrial applications, where everything can be planned in advance, to service, intervention, and exploration applications where robots must be able to address a large diversity of tasks while reacting to unforeseen situations [95] – in particular ”
Learning of grasp selection based on shape-templates (2013, methodology): “…large amounts of labeled training data which can be acquired either by evaluating grasps on a real robotic sys-tem (Montesano and Lopes 2009; Detry et al. 2010; Erkan et al. 2010) or from synthetic sensor data with a manually chosen label (Boularias et al. 2011; Bohg and Kragic 2009; Stark…”
Continuous-time Gaussian process motion planning via probabilistic inference (2017, methodology): “A similar view has been explored before by Toussaint et al. (Toussaint 2009; Toussaint and Goerick 2010).”
Automated Planning and Acting (2016, background): “This form of learning through teleoperation, where the teacher acts directly in the actuators and proprioceptive sensor spaces of the robot, is quite successful (see [537, 475] and the survey of [24]), including for quite complex tasks such as helicopter acrobatics [2, 125].”
Deliberation for autonomous robots: A survey (2017, background): “• Sensory-motor control models, as in [47, 121, 120, 164], and most of the work surveyed in [197];”