A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
A new reinforcement learning method reduces action complexity for robust control.
We study local control of the mechanism with the growth vector (4,7). We study controllability and extremal trajectories on the nilpotent approximation as an example of the control theory on Lie group. We give solutions of the system an show examples of local extremal trajectories.
In this paper, we propose a reinforcement learning-based algorithm for trajectory optimization for constrained dynamical systems. This problem is motivated by the fact that for most robotic systems, the dynamics may not always be known. Generating smooth, dynamically feasible trajectories could be difficult for such sy…
We consider in this paper the regularity problem for time-optimal trajectories of a single-input control-affine system on a n-dimensional manifold. We prove that, under generic conditions on the drift and the controlled vector field, any control u associated with an optimal trajectory is smooth out of a countable set o…
This work enables UAVs to autonomously form desired trajectories without needing a central plan.
This work presents a methodology to design trajectory tracking feedback control laws, which embed non-parametric statistical models, such as Gaussian Processes (GPs). The aim is to minimize unmodeled dynamics such as undesired slippages. The proposed approach has the benefit of avoiding complex terramechanics analysis …
The problem of continuous inverse optimal control (over finite time horizon) is to learn the unknown cost function over the sequence of continuous control variables from expert demonstrations. In this article, we study this fundamental problem in the framework of energy-based model, where the observed expert trajectori…
Learning optimal feedback control laws capable of executing optimal trajectories is essential for many robotic applications. Such policies can be learned using reinforcement learning or planned using optimal control. While reinforcement learning is sample inefficient, optimal control only plans an optimal trajectory fr…
Optimal tracking of nonholonomic systems using geometric methods.
We study control systems invariant under a Lie group with application to the problem of nonlinear trajectory planning. A theory of symmetry reduction of exterior differential systems is employed to demonstrate how symmetry reduction and reconstruction is effective in the explicit, exact construction of planned system t…
Physics-informed learning framework for pH systems and EB-PBC control.
DMPC combines MPC and value function estimation for efficient control tasks.
Study of 2D Lorentzian anti-de Sitter plane using geometric control theory.
Learning weights in a spiking neural network with hidden neurons, using local, stable and online rules, to control non-linear body dynamics is an open problem. Here, we employ a supervised scheme, Feedback-based Online Local Learning Of Weights (FOLLOW), to train a network of heterogeneous spiking neurons with hidden l…
A large body of animation research focuses on optimization of movement control, either as action sequences or policy parameters. However, as closed-form expressions of the objective functions are often not available, our understanding of the optimization problems is limited. Building on recent work on analyzing neural …
ToolChain-CRC addresses the risk-control problem for retrieval-augmented and tool-using agents under drift.
Develops a new model for controllable and realistic traffic simulation.
New algorithms learn stability certificates from data, avoiding complex dynamics.
Study optimal paths in Zermelo's navigation problem using geometric equations.
A new framework uses stochastic optimal control to estimate rare events more accurately.
Paper optimizes UAV-assisted mobile edge computing for energy efficiency.
Reinforcement Learning optimizes low-thrust interplanetary trajectories under disturbances.
This paper addresses the problem of learning the optimal control policy for a nonlinear stochastic dynamical system with continuous state space, continuous action space and unknown dynamics. This class of problems are typically addressed in stochastic adaptive control and reinforcement learning literature using model-b…
This work frames active inference through control as inference, offering robust control algorithms.
Extends RL to random stopping times, improving optimization.
Paper characterizes optimal learning trajectories for high-dimensional nonlinear models.
Model based predictions of future trajectories of a dynamical system often suffer from inaccuracies, forcing model based control algorithms to re-plan often, thus being computationally expensive, suboptimal and not reliable. In this work, we propose a model agnostic method for estimating the uncertainty of a model?s pr…
Deep neural network learns optimal trading controls for high-frequency finance.
Study on estimating unstable open-loop matrices from state trajectories.
Variational inference improves training of generative flow networks.
PhysVarMix predicts diverse urban trajectories with physics constraints.
Extends driving model to control agent behavior in simulations.
The paper proves that linearization along trajectories preserves flatness in discrete-time systems.
The purpose of this paper is to use the framework of Lie algebroids to study optimal control problems for affine connection control systems on Lie groups. In this context, the equations for critical trajectories of the problem are geometrically characterized as a Hamiltonian vector field.
Proposes SSC for estimating counterfactual survival trajectories from observational data.
Study compares RL and DT-based control for hedging European call options.
Trajectory optimization using a learned model of the environment is one of the core elements of model-based reinforcement learning. This procedure often suffers from exploiting inaccuracies of the learned model. We propose to regularize trajectory optimization by means of a denoising autoencoder that is trained on the …
This work reviews left-invariant optimal control problems on Lie groups.
CEM-GD combines CEM and gradient descent for efficient model-based RL.
This work provides safety guarantees for iterative GP predictions.
Develops a numerical algorithm for stochastic impulse control using regression surrogates.
Policy gradient methods have demonstrated success in reinforcement learning tasks that have high-dimensional continuous state and action spaces. However, policy gradient methods are also notoriously sample inefficient. This can be attributed, at least in part, to the high variance in estimating the gradient of the task…
CitySim dataset captures vehicle trajectories for safety research.
We show an example providing a significance in geometric control theory of the existence of the dependence locus of a system of vector fields in particular, the generic appearance of non-trivial singular trajectories embedded in the dependence locus.
Policy optimization is an effective reinforcement learning approach to solve continuous control tasks. Recent achievements have shown that alternating online and offline optimization is a successful choice for efficient trajectory reuse. However, deciding when to stop optimizing and collect new trajectories is non-triv…
We use the methods of geometric control theory to study extremal trajectories of vertical rolling disk. We focus on the role of symmetries of the underlying geometric structures. We demonstrate the computations in the CAS Maple package DifferentialGeometry.
DeepRacing uses neural networks to predict trajectories for autonomous racing in video games.
We study the problem of discriminative sub-trajectory mining. Given two groups of trajectories, the goal of this problem is to extract moving patterns in the form of sub-trajectories which are more similar to sub-trajectories of one group and less similar to those of the other. We propose a new method called Statistica…