466 research outputs found
Fabrics: A Foundationally Stable Medium for Encoding Prior Experience
Most dynamics functions are not well-aligned to task requirements.
Controllers, therefore, often invert the dynamics and reshape it into something
more useful. The learning community has found that these controllers, such as
Operational Space Control (OSC), can offer important inductive biases for
training. However, OSC only captures straight line end-effector motion. There's
a lot more behavior we could and should be packing into these systems. Earlier
work [15][16][19] developed a theory that generalized these ideas and
constructed a broad and flexible class of second-order dynamical systems which
was simultaneously expressive enough to capture substantial behavior (such as
that listed above), and maintained the types of stability properties that make
OSC and controllers like it a good foundation for policy design and learning.
This paper, motivated by the empirical success of the types of fabrics used in
[20], reformulates the theory of fabrics into a form that's more general and
easier to apply to policy learning problems. We focus on the stability
properties that make fabrics a good foundation for policy synthesis. Fabrics
create a fundamentally stable medium within which a policy can operate; they
influence the system's behavior without preventing it from achieving tasks
within its constraints. When a fabrics is geometric (path consistent) we can
interpret the fabric as forming a road network of paths that the system wants
to follow at constant speed absent a forcing policy, giving geometric intuition
to its role as a prior. The policy operating over the geometric fabric acts to
modulate speed and steers the system from one road to the next as it
accomplishes its task. We reformulate the theory of fabrics here rigorously and
develop theoretical results characterizing system behavior and illuminating how
to design these systems, while also emphasizing intuition throughout
Multi-Abstractive Neural Controller: An Efficient Hierarchical Control Architecture for Interactive Driving
As learning-based methods make their way from perception systems to
planning/control stacks, robot control systems have started to enjoy the
benefits that data-driven methods provide. Because control systems directly
affect the motion of the robot, data-driven methods, especially black box
approaches, need to be used with caution considering aspects such as stability
and interpretability. In this paper, we describe a differentiable and
hierarchical control architecture. The proposed representation, called
\textit{multi-abstractive neural controller}, uses the input image to control
the transitions within a novel discrete behavior planner (referred to as the
visual automaton generative network, or \textit{vAGN}). The output of a vAGN
controls the parameters of a set of dynamic movement primitives which provides
the system controls. We train this neural controller with real-world driving
data via behavior cloning and show improved explainability, sample efficiency,
and similarity to human driving
Learning Latent Space Dynamics for Tactile Servoing
To achieve a dexterous robotic manipulation, we need to endow our robot with
tactile feedback capability, i.e. the ability to drive action based on tactile
sensing. In this paper, we specifically address the challenge of tactile
servoing, i.e. given the current tactile sensing and a target/goal tactile
sensing --memorized from a successful task execution in the past-- what is the
action that will bring the current tactile sensing to move closer towards the
target tactile sensing at the next time step. We develop a data-driven approach
to acquire a dynamics model for tactile servoing by learning from
demonstration. Moreover, our method represents the tactile sensing information
as to lie on a surface --or a 2D manifold-- and perform a manifold learning,
making it applicable to any tactile skin geometry. We evaluate our method on a
contact point tracking task using a robot equipped with a tactile finger. A
video demonstrating our approach can be seen in https://youtu.be/0QK0-Vx7WkIComment: Accepted to be published at the International Conference on Robotics
and Automation (ICRA) 2019. The final version for publication at ICRA 2019 is
7 pages (i.e. 6 pages of technical content (including text, figures, tables,
acknowledgement, etc.) and 1 page of the Bibliography/References), while this
arXiv version is 8 pages (added Appendix and some extra details
QuaDUE-CCM: Interpretable Distributional Reinforcement Learning using Uncertain Contraction Metrics for Precise Quadrotor Trajectory Tracking
Accuracy and stability are common requirements for Quadrotor trajectory
tracking systems. Designing an accurate and stable tracking controller remains
challenging, particularly in unknown and dynamic environments with complex
aerodynamic disturbances. We propose a Quantile-approximation-based
Distributional-reinforced Uncertainty Estimator (QuaDUE) to accurately identify
the effects of aerodynamic disturbances, i.e., the uncertainties between the
true and estimated Control Contraction Metrics (CCMs). Taking inspiration from
contraction theory and integrating the QuaDUE for uncertainties, our novel
CCM-based trajectory tracking framework tracks any feasible reference
trajectory precisely whilst guaranteeing exponential convergence. More
importantly, the convergence and training acceleration of the distributional RL
are guaranteed and analyzed, respectively, from theoretical perspectives. We
also demonstrate our system under unknown and diverse aerodynamic forces. Under
large aerodynamic forces (>2m/s^2), compared with the classic data-driven
approach, our QuaDUE-CCM achieves at least a 56.6% improvement in tracking
error. Compared with QuaDRED-MPC, a distributional RL-based approach,
QuaDUE-CCM achieves at least a 3 times improvement in contraction rate.Comment: 18 pages, 9 figures, Quadrotor trajectory tracking, Learning-based
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