38,818 research outputs found
Robot Composite Learning and the Nunchaku Flipping Challenge
Advanced motor skills are essential for robots to physically coexist with
humans. Much research on robot dynamics and control has achieved success on
hyper robot motor capabilities, but mostly through heavily case-specific
engineering. Meanwhile, in terms of robot acquiring skills in a ubiquitous
manner, robot learning from human demonstration (LfD) has achieved great
progress, but still has limitations handling dynamic skills and compound
actions. In this paper, we present a composite learning scheme which goes
beyond LfD and integrates robot learning from human definition, demonstration,
and evaluation. The method tackles advanced motor skills that require dynamic
time-critical maneuver, complex contact control, and handling partly soft
partly rigid objects. We also introduce the "nunchaku flipping challenge", an
extreme test that puts hard requirements to all these three aspects. Continued
from our previous presentations, this paper introduces the latest update of the
composite learning scheme and the physical success of the nunchaku flipping
challenge
Gaussian-Process-based Robot Learning from Demonstration
Endowed with higher levels of autonomy, robots are required to perform
increasingly complex manipulation tasks. Learning from demonstration is arising
as a promising paradigm for transferring skills to robots. It allows to
implicitly learn task constraints from observing the motion executed by a human
teacher, which can enable adaptive behavior. We present a novel
Gaussian-Process-based learning from demonstration approach. This probabilistic
representation allows to generalize over multiple demonstrations, and encode
variability along the different phases of the task. In this paper, we address
how Gaussian Processes can be used to effectively learn a policy from
trajectories in task space. We also present a method to efficiently adapt the
policy to fulfill new requirements, and to modulate the robot behavior as a
function of task variability. This approach is illustrated through a real-world
application using the TIAGo robot.Comment: 8 pages, 10 figure
A Multimodal Hierarchial Approach to Robot Learning by Imitation
In this paper we propose an approach to robot learning by imitation that uses the multimodal inputs of language, vision and motor. In our approach a student robot learns from a teacher robot how to perform three separate behaviours based on these inputs. We considered two neural architectures for performing this robot learning. First, a one-step hierarchial architecture trained with two different learning approaches either based on Kohonen's self-organising map or based on the Helmholtz machine turns out to be inefficient or not capable of performing differentiated behavior. In response we produced a hierarchial architecture that combines both learning approaches to overcome these problems. In doing so the proposed robot system models specific aspects of learning using concepts of the mirror neuron system (Rizzolatti and Arbib, 1998) with regards to demonstration learning
Multiform Adaptive Robot Skill Learning from Humans
Object manipulation is a basic element in everyday human lives. Robotic
manipulation has progressed from maneuvering single-rigid-body objects with
firm grasping to maneuvering soft objects and handling contact-rich actions.
Meanwhile, technologies such as robot learning from demonstration have enabled
humans to intuitively train robots. This paper discusses a new level of robotic
learning-based manipulation. In contrast to the single form of learning from
demonstration, we propose a multiform learning approach that integrates
additional forms of skill acquisition, including adaptive learning from
definition and evaluation. Moreover, going beyond state-of-the-art technologies
of handling purely rigid or soft objects in a pseudo-static manner, our work
allows robots to learn to handle partly rigid partly soft objects with
time-critical skills and sophisticated contact control. Such capability of
robotic manipulation offers a variety of new possibilities in human-robot
interaction.Comment: Accepted to 2017 Dynamic Systems and Control Conference (DSCC),
Tysons Corner, VA, October 11-1
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