15 research outputs found
Learning Sensor Feedback Models from Demonstrations via Phase-Modulated Neural Networks
In order to robustly execute a task under environmental uncertainty, a robot
needs to be able to reactively adapt to changes arising in its environment. The
environment changes are usually reflected in deviation from expected sensory
traces. These deviations in sensory traces can be used to drive the motion
adaptation, and for this purpose, a feedback model is required. The feedback
model maps the deviations in sensory traces to the motion plan adaptation. In
this paper, we develop a general data-driven framework for learning a feedback
model from demonstrations. We utilize a variant of a radial basis function
network structure --with movement phases as kernel centers-- which can
generally be applied to represent any feedback models for movement primitives.
To demonstrate the effectiveness of our framework, we test it on the task of
scraping on a tilt board. In this task, we are learning a reactive policy in
the form of orientation adaptation, based on deviations of tactile sensor
traces. As a proof of concept of our method, we provide evaluations on an
anthropomorphic robot. A video demonstrating our approach and its results can
be seen in https://youtu.be/7Dx5imy1KcwComment: 8 pages, accepted to be published at the International Conference on
Robotics and Automation (ICRA) 201
Learning Task Constraints from Demonstration for Hybrid Force/Position Control
We present a novel method for learning hybrid force/position control from
demonstration. We learn a dynamic constraint frame aligned to the direction of
desired force using Cartesian Dynamic Movement Primitives. In contrast to
approaches that utilize a fixed constraint frame, our approach easily
accommodates tasks with rapidly changing task constraints over time. We
activate only one degree of freedom for force control at any given time,
ensuring motion is always possible orthogonal to the direction of desired
force. Since we utilize demonstrated forces to learn the constraint frame, we
are able to compensate for forces not detected by methods that learn only from
the demonstrated kinematic motion, such as frictional forces between the
end-effector and the contact surface. We additionally propose novel extensions
to the Dynamic Movement Primitive (DMP) framework that encourage robust
transition from free-space motion to in-contact motion in spite of environment
uncertainty. We incorporate force feedback and a dynamically shifting goal to
reduce forces applied to the environment and retain stable contact while
enabling force control. Our methods exhibit low impact forces on contact and
low steady-state tracking error.Comment: Under revie
A knowledge-based framework for task automation in surgery
Robotic surgery has significantly improved the quality of surgical procedures. In the past, researches have been focused on automating simple surgical actions, however there exists no scalable framework for automation in surgery. In this paper, we present a knowledge-based modular framework for the automation of articulated surgical tasks, for example, with multiple coordinated actions. The framework is consisted of ontology, providing entities for surgical automation and rules for task planning, and \u201cdynamic movement primitives\u201d as adaptive motion planner as to replicate the dexterity of surgeons. To validate our framework, we chose a paradigmatic scenario of a peg-and-ring task, a standard training exercise for novice surgeons which presents many challenges of real surgery, e.g. grasping and transferring. Experiments show the validity of the framework and its adaptability to faulty events. The modular architecture is expected to generalize to different tasks and platforms
Learning Barrier Functions for Constrained Motion Planning with Dynamical Systems
Stable dynamical systems are a flexible tool to plan robotic motions in
real-time. In the robotic literature, dynamical system motions are typically
planned without considering possible limitations in the robot's workspace. This
work presents a novel approach to learn workspace constraints from human
demonstrations and to generate motion trajectories for the robot that lie in
the constrained workspace. Training data are incrementally clustered into
different linear subspaces and used to fit a low dimensional representation of
each subspace. By considering the learned constraint subspaces as zeroing
barrier functions, we are able to design a control input that keeps the system
trajectory within the learned bounds. This control input is effectively
combined with the original system dynamics preserving eventual asymptotic
properties of the unconstrained system. Simulations and experiments on a real
robot show the effectiveness of the proposed approach
Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functions
Obstacle avoidance for DMPs is still a challenging problem. In our previous
work, we proposed a framework for obstacle avoidance based on superquadric
potential functions to represent volumes. In this work, we extend our previous
work to include the velocity of the trajectory in the definition of the
potential. Our formulations guarantee smoother behavior with respect to
state-of-the-art point-like methods. Moreover, our new formulation allows to
obtain a smoother behavior in proximity of the obstacle than when using a
static (i.e. velocity independent) potential. We validate our framework for
obstacle avoidance in a simulated multi-robot scenario and with different real
robots: a pick-and-place task for an industrial manipulator and a surgical
robot to show scalability; and navigation with a mobile robot in dynamic
environment.Comment: Preprint for Journal of Intelligent and Robotic System
Robot skill learning system of multi-space fusion based on dynamic movement primitives and adaptive neural network control
This article develops a robot skill learning system with multi-space fusion, simultaneously considering motion/stiffness generation and trajectory tracking. To begin with, surface electromyography (sEMG) signals from the human arm is captured based on the MYO armband to estimate endpoint stiffness. Gaussian Process Regression (GPR) is combined with dynamic movement primitive (DMP) to extract more skills features from multi-demonstrations. Then, the traditional DMP formulation is improved based on the Riemannian metric to encode the robot's quaternions with non-Euclidean properties. Furthermore, an adaptive neural network (NN)-based finite-time admittance controller is designed to track the trajectory generated by the motion model and to reflect the learned stiffness characteristics. In this controller, a radial basis function neural network (RBFNN) is employed to compensate for the uncertainty of the robot dynamics. Finally, experimental validation is conducted using the ROKAE collaborative robot, confirming the effectiveness of the proposed approach. In summary, the presented framework is suitable for human-robot skill transfer method that require simultaneous consideration of position and stiffness in Euclidean space, as well as orientation on Riemannian manifolds