8,373 research outputs found
How to Deploy a Wire with a Robotic Platform: Learning from Human Visual Demonstrations
In this paper, we address the problem of deploying a wire along a specific path selected by an unskilled user. The robot has to
learn the selected path and pass a wire through the peg table by using the same tool. The main contribution regards the hybrid use
of Cartesian positions provided by a learning procedure and joint positions obtained by inverse kinematics and motion planning.
Some constraints are introduced to deal with non-rigid material without breaks or knots. We took into account a series of metrics
to evaluate the robot learning capabilities, all of them over performed the targets
Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior
We aim to reduce the burden of programming and deploying autonomous systems
to work in concert with people in time-critical domains, such as military field
operations and disaster response. Deployment plans for these operations are
frequently negotiated on-the-fly by teams of human planners. A human operator
then translates the agreed upon plan into machine instructions for the robots.
We present an algorithm that reduces this translation burden by inferring the
final plan from a processed form of the human team's planning conversation. Our
approach combines probabilistic generative modeling with logical plan
validation used to compute a highly structured prior over possible plans. This
hybrid approach enables us to overcome the challenge of performing inference
over the large solution space with only a small amount of noisy data from the
team planning session. We validate the algorithm through human subject
experimentation and show we are able to infer a human team's final plan with
83% accuracy on average. We also describe a robot demonstration in which two
people plan and execute a first-response collaborative task with a PR2 robot.
To the best of our knowledge, this is the first work that integrates a logical
planning technique within a generative model to perform plan inference.Comment: Appears in Proceedings of the Twenty-Seventh AAAI Conference on
Artificial Intelligence (AAAI-13
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
An Uncertainty-Aware Minimal Intervention Control Strategy Learned from Demonstrations
Motivated by the desire to have robots physically present in human environments, in recent years we have witnessed an emergence of different approaches for learning active compliance. Some of the most compelling solutions exploit a minimal intervention control principle, correcting deviations from a goal only when necessary, and among those who follow this concept, several probabilistic techniques have stood out from the rest. However, these approaches are prone to requiring several task demonstrations for proper gain estimation and to generating unpredictable robot motions in the face of uncertainty. Here we present a Programming by Demonstration approach for uncertainty-aware impedance regulation, aimed at making the robot compliant - and safe to interact with - when the uncertainty about its predicted actions is high. Moreover, we propose a data-efficient strategy, based on the energy observed during demonstrations, to achieve minimal intervention control, when the uncertainty is low. The approach is validated in an experimental scenario, where a human collaboratively moves an object with a 7-DoF torque-controlled robot
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