3,853 research outputs found
Neural Lyapunov Control
We propose new methods for learning control policies and neural network
Lyapunov functions for nonlinear control problems, with provable guarantee of
stability. The framework consists of a learner that attempts to find the
control and Lyapunov functions, and a falsifier that finds counterexamples to
quickly guide the learner towards solutions. The procedure terminates when no
counterexample is found by the falsifier, in which case the controlled
nonlinear system is provably stable. The approach significantly simplifies the
process of Lyapunov control design, provides end-to-end correctness guarantee,
and can obtain much larger regions of attraction than existing methods such as
LQR and SOS/SDP. We show experiments on how the new methods obtain high-quality
solutions for challenging control problems.Comment: NeurIPS 201
Discrete mechanics and optimal control for constrained systems
The equations of motion of a controlled mechanical system subject to holonomic constraints may be formulated in terms
of the states and controls by applying a constrained version of the Lagrange-dāAlembert principle. This paper derives a
structure-preserving scheme for the optimal control of such systems using, as one of the key ingredients, a discrete analogue
of that principle. This property is inherited when the system is reduced to its minimal dimension by the discrete null
space method. Together with initial and final conditions on the configuration and conjugate momentum, the reduced discrete
equations serve as nonlinear equality constraints for the minimization of a given objective functional. The algorithm yields
a sequence of discrete configurations together with a sequence of actuating forces, optimally guiding the system from the
initial to the desired final state. In particular, for the optimal control of multibody systems, a force formulation consistent
with the joint constraints is introduced. This enables one to prove the consistency of the evolution of momentum maps.
Using a two-link pendulum, the method is compared with existing methods. Further, it is applied to a satellite reorientation
maneuver and a biomotion problem
Benchmarking Deep Reinforcement Learning for Continuous Control
Recently, researchers have made significant progress combining the advances
in deep learning for learning feature representations with reinforcement
learning. Some notable examples include training agents to play Atari games
based on raw pixel data and to acquire advanced manipulation skills using raw
sensory inputs. However, it has been difficult to quantify progress in the
domain of continuous control due to the lack of a commonly adopted benchmark.
In this work, we present a benchmark suite of continuous control tasks,
including classic tasks like cart-pole swing-up, tasks with very high state and
action dimensionality such as 3D humanoid locomotion, tasks with partial
observations, and tasks with hierarchical structure. We report novel findings
based on the systematic evaluation of a range of implemented reinforcement
learning algorithms. Both the benchmark and reference implementations are
released at https://github.com/rllab/rllab in order to facilitate experimental
reproducibility and to encourage adoption by other researchers.Comment: 14 pages, ICML 201
Fast Model Identification via Physics Engines for Data-Efficient Policy Search
This paper presents a method for identifying mechanical parameters of robots
or objects, such as their mass and friction coefficients. Key features are the
use of off-the-shelf physics engines and the adaptation of a Bayesian
optimization technique towards minimizing the number of real-world experiments
needed for model-based reinforcement learning. The proposed framework
reproduces in a physics engine experiments performed on a real robot and
optimizes the model's mechanical parameters so as to match real-world
trajectories. The optimized model is then used for learning a policy in
simulation, before real-world deployment. It is well understood, however, that
it is hard to exactly reproduce real trajectories in simulation. Moreover, a
near-optimal policy can be frequently found with an imperfect model. Therefore,
this work proposes a strategy for identifying a model that is just good enough
to approximate the value of a locally optimal policy with a certain confidence,
instead of wasting effort on identifying the most accurate model. Evaluations,
performed both in simulation and on a real robotic manipulation task, indicate
that the proposed strategy results in an overall time-efficient, integrated
model identification and learning solution, which significantly improves the
data-efficiency of existing policy search algorithms.Comment: IJCAI 1
Learning a Unified Control Policy for Safe Falling
Being able to fall safely is a necessary motor skill for humanoids performing
highly dynamic tasks, such as running and jumping. We propose a new method to
learn a policy that minimizes the maximal impulse during the fall. The
optimization solves for both a discrete contact planning problem and a
continuous optimal control problem. Once trained, the policy can compute the
optimal next contacting body part (e.g. left foot, right foot, or hands),
contact location and timing, and the required joint actuation. We represent the
policy as a mixture of actor-critic neural network, which consists of n control
policies and the corresponding value functions. Each pair of actor-critic is
associated with one of the n possible contacting body parts. During execution,
the policy corresponding to the highest value function will be executed while
the associated body part will be the next contact with the ground. With this
mixture of actor-critic architecture, the discrete contact sequence planning is
solved through the selection of the best critics while the continuous control
problem is solved by the optimization of actors. We show that our policy can
achieve comparable, sometimes even higher, rewards than a recursive search of
the action space using dynamic programming, while enjoying 50 to 400 times of
speed gain during online execution
Augmenting Sensorimotor Control Using āGoal-Awareā Vibrotactile Stimulation during Reaching and Manipulation Behaviors
We describe two sets of experiments that examine the ability of vibrotactile encoding of simple position error and combined object states (calculated from an optimal controller) to enhance performance of reaching and manipulation tasks in healthy human adults. The goal of the first experiment (tracking) was to follow a moving target with a cursor on a computer screen. Visual and/or vibrotactile cues were provided in this experiment, and vibrotactile feedback was redundant with visual feedback in that it did not encode any information above and beyond what was already available via vision. After only 10 minutes of practice using vibrotactile feedback to guide performance, subjects tracked the moving target with response latency and movement accuracy values approaching those observed under visually guided reaching. Unlike previous reports on multisensory enhancement, combining vibrotactile and visual feedback of performance errors conferred neither positive nor negative effects on task performance. In the second experiment (balancing), vibrotactile feedback encoded a corrective motor command as a linear combination of object states (derived from a linear-quadratic regulator implementing a trade-off between kinematic and energetic performance) to teach subjects how to balance a simulated inverted pendulum. Here, the tactile feedback signal differed from visual feedback in that it provided information that was not readily available from visual feedback alone. Immediately after applying this novel āgoal-awareā vibrotactile feedback, time to failure was improved by a factor of three. Additionally, the effect of vibrotactile training persisted after the feedback was removed. These results suggest that vibrotactile encoding of appropriate combinations of state information may be an effective form of augmented sensory feedback that can be applied, among other purposes, to compensate for lost or compromised proprioception as commonly observed, for example, in stroke survivors
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