arXiv subjects

Jean-Jacques Slotine

Publications and source records attributed to Jean-Jacques Slotine.

At least 19 recordsLinked to original sources

arXiv2026

Interpreting Bohm-like quantum potentials in "Computing quantum waves exactly from classical action"

In contrast to his earlier posting arXiv260502621 [6] commenting on the article rspa20250413 [5] and answered in arXiv260520443 [3] the same author of the new posting arXiv260605197 [7] does not seem to dispute any longer the following points i Assuming in [5] that along each extremal action path the action Laplacian or more generally the propagated density is space independent implies that the Bohm like potential terms are exactly zero a condition which is directly verified in all examples of [5] as well as in its relativistic Dirac and Maxwell extensions ii Just as in Feynmans well known results the standard polynomials in the treatment in [5] of the harmonic oscillator appear naturally from the Taylor expansion of the kernel and thus there is no circularity. He does not appear to dispute either that Bohm like terms along individual stationary action paths are indeed very different from the standard Bohm Madelung potential on the overall wave. This is illustrated e.g. in the detailed double slit example in [3] whose Bohm like terms are exactly zero while the usual Bohm Madelung potential can be very large. Also in that example the action behind the slits is a conic function so that exact Feynman or Van Vleck computations based on quadratic actions do not apply. His new posting [7] therefore concentrates on the claim in [3] that the space independence assumption on the Laplacian of the action if not directly verified can be achieved through time rescaling which is a fair discussion topic We all agree that the Schrodinger equation has to be fulfilled in the original x t coordinates. To this effect as in the hydrogen atom example of the original paper [5] we use a general result of Duru and Kleinert which the author of [6] and [7] may be unaware of. There independence of the constructed eigenwave on the rescaled time is key to the exact backmapping to the original x t coordinates.

quant-ph↗

arXiv2026

Riemannian Optimization in Modular Systems

Understanding how systems built out of modular components can be jointly optimized is an important problem in biology, engineering, and machine learning. The backpropagation algorithm is one such solution and has been instrumental in the success of neural networks. Despite its empirical success, a strong theoretical understanding of it is lacking. Here, we combine tools from Riemannian geometry, optimal control theory, and theoretical physics to advance this understanding. We make three key contributions: First, we revisit the derivation of backpropagation as a constrained optimization problem and combine it with the insight that Riemannian gradient descent trajectories can be understood as the minimum of an action. Second, we introduce a recursively defined layerwise Riemannian metric that exploits the modular structure of neural networks and can be efficiently computed using the Woodbury matrix identity, avoiding the $O(n^3)$ cost of full metric inversion. Third, we develop a framework of composable ``Riemannian modules'' whose convergence properties can be quantified using nonlinear contraction theory, providing algorithmic stability guarantees of order $O(\kappa^2 L/(\xi \mu \sqrt{n}))$ where $\kappa$ and $L$ are Lipschitz constants, $\mu$ is the mass matrix scale, and $\xi$ bounds the condition number. Our layerwise metric approach provides a practical alternative to natural gradient descent. While we focus here on studying neural networks, our approach more generally applies to the study of systems made of modules that are optimized over time, as it occurs in biology during both evolution and development.

cs.LG↗

arXiv2026

Unlocked Backpropagation using Wave Scattering

Both the backpropagation algorithm in machine learning and the maximum principle in optimal control theory are posed as a two-point boundary problem, resulting in a "forward-backward" lock. We derive a reformulation of the maximum principle in optimal control theory as a hyperbolic initial value problem by introducing an additional "optimization time" dimension. We introduce counter-propagating wave variables with finite propagation speed and recast the optimization problem in terms of scattering relationships between them. This relaxation of the original problem can be interpreted as a physical system that equilibrates and changes its physical properties in order to minimize reflections. We discretize this continuum theory to derive a family of fully unlocked algorithms suitable for training neural networks. Different parameter dynamics, including gradient descent, can be derived by demanding dissipation and minimization of reflections at parameter ports. These results also imply that any physical substrate that supports the scattering and dissipation of waves can be interpreted as solving an optimization problem.

math.OC↗

arXiv2025

NRGPT: An Energy-based Alternative for GPT

Generative Pre-trained Transformer (GPT) architectures are the most popular design for language modeling. Energy-based modeling is a different paradigm that views inference as a dynamical process operating on an energy landscape. We propose a minimal modification of the GPT setting to unify it with the EBM framework. The inference step of our model, which we call eNeRgy-GPT (NRGPT), is conceptualized as an exploration of the tokens on the energy landscape. We prove, and verify empirically, that under certain circumstances this exploration becomes gradient descent, although they don't necessarily lead to the best performing models. We demonstrate that our model performs well for simple language (Shakespeare dataset), algebraic ListOPS tasks, and richer settings such as OpenWebText language modeling. We also observe that our models may be more resistant to overfitting, doing so only during very long training.

cs.LG↗

arXiv2025

Thermodynamic structure of the Sinkhorn flow

Entropy-regularized optimal transport, which has strong links to the Schr\"odinger bridge problem in statistical mechanics, enjoys a variety of applications from trajectory inference to generative modeling. A major driver of renewed interest in this problem is the recent development of fast matrix-scaling algorithms\textemdash known as iterative proportional fitting or the Sinkhorn algorithm\textemdash for entropic optimal transport, which have favorable complexity over traditional approaches to the unregularized problem. Here, we take a perspective on this algorithm rooted in the thermodynamic origins of Schr\"odinger's problem and inspired by the modern geometric theory of diffusion: is the Sinkhorn flow (viewed in continuous-time as a mirror descent by recent results) the gradient flow of entropy in a formal Riemannian geometry? We answer this question affirmatively, finding a nonlocal Wasserstein gradient structure in the dynamics of its free marginal. This offers a physical interpretation of the Sinkhorn flow as the stochastic dynamics of a particle with law evolving by the nonlocal diffusion of a chemical potential. Simultaneously, it brings a standard suite of functional inequalities characterizing Markov diffusion processes to bear upon its geometry and convergence. We prove an entropy-energy (de Bruijn) identity, a Poincar\'e inequality, and a Bakry-\'Emery-type condition under which a logarithmic Sobolev inequality (LSI) holds and implies exponential convergence of the Sinkhorn flow in entropy. We lastly discuss computational applications such as stopping heuristics and latent-space design criteria leveraging the LSI and, returning to the physical interpretation, the possibility of natural systems whose relaxation to equilibrium inherently solves entropic optimal transport or Schr\"odinger bridge problems.

stat.ML↗

arXiv2025

Quantum Optimal Control for Coherent Spin Dynamics of Radical Pairs via Pontryagin Maximum Principle

This paper aims to devise the shape of the external electromagnetic field that drives the spin dynamics of radical pairs to a quantum coherent state through maximization of the triplet-born singlet yield in biochemical reactions. The model is a Schr\"{o}dinger system with spin Hamiltonians given by the sum of Zeeman interaction and hyperfine coupling interaction terms. We introduce a one-parameter family of optimal control problems by coupling the Schr\"{o}dinger system to a control field through filtering equations for the electromagnetic field. Fr\'echet differentiability and the Pontryagin Maximum Principle in Hilbert space are proved, and the bang-bang structure of the optimal control is established. A new iterative Pontryagin Maximum Principle (IPMP) method for the identification of the bang-bang optimal control is developed. Numerical simulations based on IPMP and the gradient projection method (GPM) in Hilbert spaces are pursued, and the convergence, stability, and the regularization effect are demonstrated. Comparative analysis of filtering with regular optimal electromagnetic field versus non-filtering with bang-bang optimal field ({\it Abdulla et al, Quantum Sci. Technol., {\bf9}, 4, 2024}) demonstrates that the change of the maxima of the singlet yield is less than 1\%. The results open a venue for a potential experimental work on magnetoreception as a manifestation of quantum biological phenomena.

quant-ph↗

arXiv2025

Contraction Dynamics in Heterogeneous Spatial Environments

Understanding the asymptotic behavior of reaction-diffusion (RD) systems is crucial for modeling processes ranging from species coexistence in ecology to biochemical interactions within cells. In this work, we analyze RD systems in which diffusion is modeled using the $\theta$-diffusion framework, while the reaction dynamics are spatially varying. We demonstrate that spatial heterogeneity affects the asymptotic behavior of such systems. Using contraction theory, we derive conditions that guarantee the exponential convergence of system trajectories, regardless of initial conditions. These conditions explicitly account for the influence of spatial heterogeneity in both the diffusion and reaction terms. As an application, we study a biochemical system and derive the quasi-steady-state (QSS) approximation, illustrating how spatial heterogeneity modulates the effective binding rates of biomolecular species.

math.DS↗

arXiv2025

Combining Movement Primitives with Contraction Theory

This paper presents a modular framework for motion planning using movement primitives. Central to the approach is Contraction Theory, a modular stability tool for nonlinear dynamical systems. The approach extends prior methods by achieving parallel and sequential combinations of both discrete and rhythmic movements, while enabling independent modulation of each movement. This modular framework enables a divide-and-conquer strategy to simplify the programming of complex robot motion planning. Simulation examples illustrate the flexibility and versatility of the framework, highlighting its potential to address diverse challenges in robot motion planning.

cs.RO↗

arXiv2024

Quaternion Sliding Variables in Manipulator Control

We present two quaternion-based sliding variables for controlling the orientation of a manipulator's end-effector. Both sliding variables are free of singularities and represent global exponentially convergent error dynamics that do not exhibit unwinding when used in feedback. The choice of sliding variable is dictated by whether the end-effector's angular velocity vector is expressed in a local or global frame, and is a matter of convenience. Using quaternions allows the end-effector to move in its full operational envelope, which is not possible with other representations, e.g., Euler angles, that introduce representation-specific singularities. Further, the presented stability results are global rather than almost global, where the latter is often the best one can achieve when using rotation matrices to represent orientation.

cs.RO↗

arXiv2024

URDF+: An Enhanced URDF for Robots with Kinematic Loops

Designs incorporating kinematic loops are becoming increasingly prevalent in the robotics community. Despite the existence of dynamics algorithms to deal with the effects of such loops, many modern simulators rely on dynamics libraries that require robots to be represented as kinematic trees. This requirement is reflected in the de facto standard format for describing robots, the Universal Robot Description Format (URDF), which does not support kinematic loops resulting in closed chains. This paper introduces an enhanced URDF, termed URDF+, which addresses this key shortcoming of URDF while retaining the intuitive design philosophy and low barrier to entry that the robotics community values. The URDF+ keeps the elements used by URDF to describe open chains and incorporates new elements to encode loop joints. We also offer an accompanying parser that processes the system models coming from URDF+ so that they can be used with recursive rigid-body dynamics algorithms for closed-chain systems that group bodies into local, decoupled loops. This parsing process is fully automated, ensuring optimal grouping of constrained bodies without requiring manual specification from the user. We aim to advance the robotics community towards this elegant solution by developing efficient and easy-to-use software tools.

cs.RO↗

arXiv2024

On computing quantum waves exactly from classical and relativistic action

We show that the Schr\"odinger equation can be solved exactly based only on classical least action. Fundamental postulates of quantum mechanics can in turn be derived directly from this construction. The results extend to the relativistic Klein-Gordon, Pauli, Dirac, and Maxwell equations, and suggest a smooth transition between physics across scales. Most quantum mechanics problems have classical versions which involve multiple least action solutions. The associated classical multipaths stem either from the initial position or momentum distribution, or from branch points, generated, e.g., by a multiply connected manifold (double slit experiment), by spatial inequality constraints (particle in a box), or by a singularity (Coulomb potential). We show that the exact Schr\"odinger wave function $\psi$ of the original quantum problem can be constructed by combining this classical multi-valued action $\phi$ with the density $\rho$ of the classical position dynamics, where a key point is that $\rho$ can be easily computed from $\phi$ along each extremal action path. The construction is general and does not involve any quasi-classical approximation. Examples illustrate how the quantum wave functions for the double-slit experiment or e.g., the hydrogen atom can be computed exactly from their classical least action counterparts. In a quantum measurement process, randomness originates from the determined forward mapping of an initial classical density distribution. In the Einstein-Podolsky-Rosen experiment, while Bell's inequalities are violated, from this perspective there is indeed a hidden variable in the form of a complex spinor. These results also provide a simpler computational alternative to Feynman path integrals, as they use only a minimal subset of classical paths and avoid zig-zag paths and time-slicing altogether.

quant-ph↗

arXiv2024

Passive Obstacle Aware Control to Follow Desired Velocities

Evaluating and updating the obstacle avoidance velocity for an autonomous robot in real-time ensures robustness against noise and disturbances. A passive damping controller can obtain the desired motion with a torque-controlled robot, which remains compliant and ensures a safe response to external perturbations. Here, we propose a novel approach for designing the passive control policy. Our algorithm complies with obstacle-free zones while transitioning to increased damping near obstacles to ensure collision avoidance. This approach ensures stability across diverse scenarios, effectively mitigating disturbances. Validation on a 7DoF robot arm demonstrates superior collision rejection capabilities compared to the baseline, underlining its practicality for real-world applications. Our obstacle-aware damping controller represents a substantial advancement in secure robot control within complex and uncertain environments.

cs.RO↗

arXiv2024

Constrained Least Action and Quantum Mechanics

Recent work shows that the Schroedinger equation can be solved exactly based only on classical least action. The computation is based on solving a HamiltonJacobi equation for the action computing the classical density accordingly along all stationary action paths and finally constructing the exact wave function based on these classical quantities alone. The method requires that the action Laplacian or more generally the Laplacian of the propagated density be purely time-varying along stationary action paths. In the case of arbitrary nonlinear potentials this condition can still be verified without loss of generality by using a time rescaling. As the complexity of the wave computation is thus shifted to that of the action and the possible time rescaling this paper proposes a new analytical approach both to compute the action itself for a general nonlinear Hamilton-Jacobi pde and to concurrently construct a time rescaling as needed In contrast to solving the Schroedinger equation directly this computation of action and density extends naturally to systems with nonlinear potentials or position dependent inertia tensors. Hence in principle it can replace the approximations of quantum perturbation theory. For general nonlinear potentials, extending a result of Duru and Kleinert the time rescaling in a given metric is shown to correspond to a change of variables in the computed action density and wave with each eigenwave computed using that change of variables. We show that the approach makes it straightforward to construct the quantum wave for basic cases where no exact solution has been yet derived with the time rescaling unifying the computation. The approach first illustrated for the known three dimensional hyperbolic potential waves of the hydrogen atom is then used to compute the quantum waves for a quartic oscillator for which so far no direct exact solutions are known.

math-ph↗

arXiv2023

Neuron-Astrocyte Associative Memory

Astrocytes, the most abundant type of glial cell, play a fundamental role in memory. Despite most hippocampal synapses being contacted by an astrocyte, there are no current theories that explain how neurons, synapses, and astrocytes might collectively contribute to memory function. We demonstrate that fundamental aspects of astrocyte morphology and physiology naturally lead to a dynamic, high-capacity associative memory system. The neuron-astrocyte networks generated by our framework are closely related to popular machine learning architectures known as Dense Associative Memories or Modern Hopfield Networks. In their known biological implementations the ratio of stored memories to the number of neurons remains constant, despite the growth of the network size. Our work demonstrates that neuron-astrocyte networks follow superior, supralinear memory scaling laws, outperforming all known biological implementations of Dense Associative Memory. This theoretical link suggests the exciting and previously unnoticed possibility that memories could be stored, at least in part, within astrocytes rather than solely in the synaptic weights between neurons.

q-bio.NC↗

arXiv2023

Dynamic Adaptation Gains for Nonlinear Systems with Unmatched Uncertainties

We present a new direct adaptive control approach for nonlinear systems with unmatched and matched uncertainties. The method relies on adjusting the adaptation gains of individual unmatched parameters whose adaptation transients would otherwise destabilize the closed-loop system. The approach also guarantees the restoration of the adaptation gains to their nominal values and can readily incorporate direct adaptation laws for matched uncertainties. The proposed framework is general as it only requires stabilizability for all possible models.

eess.SY↗

arXiv2023

Stable Modular Control via Contraction Theory for Reinforcement Learning

We propose a novel way to integrate control techniques with reinforcement learning (RL) for stability, robustness, and generalization: leveraging contraction theory to realize modularity in neural control, which ensures that combining stable subsystems can automatically preserve the stability. We realize such modularity via signal composition and dynamic decomposition. Signal composition creates the latent space, within which RL applies to maximizing rewards. Dynamic decomposition is realized by coordinate transformation that creates an auxiliary space, within which the latent signals are coupled in the way that their combination can preserve stability provided each signal, that is, each subsystem, has stable self-feedbacks. Leveraging modularity, the nonlinear stability problem is deconstructed into algebraically solvable ones, the stability of the subsystems in the auxiliary space, yielding linear constraints on the input gradients of control networks that can be as simple as switching the signs of network weights. This minimally invasive method for stability allows arguably easy integration into the modular neural architectures in machine learning, like hierarchical RL, and improves their performance. We demonstrate in simulation the necessity and the effectiveness of our method: the necessity for robustness and generalization, and the effectiveness in improving hierarchical RL for manipulation learning.

cs.LG↗

arXiv2023

Contraction Properties of the Global Workspace Primitive

To push forward the important emerging research field surrounding multi-area recurrent neural networks (RNNs), we expand theoretically and empirically on the provably stable RNNs of RNNs introduced by Kozachkov et al. in "RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks". We prove relaxed stability conditions for salient special cases of this architecture, most notably for a global workspace modular structure. We then demonstrate empirical success for Global Workspace Sparse Combo Nets with a small number of trainable parameters, not only through strong overall test performance but also greater resilience to removal of individual subnetworks. These empirical results for the global workspace inter-area topology are contingent on stability preservation, highlighting the relevance of our theoretical work for enabling modular RNN success. Further, by exploring sparsity in the connectivity structure between different subnetwork modules more broadly, we improve the state of the art performance for stable RNNs on benchmark sequence processing tasks, thus underscoring the general utility of specialized graph structures for multi-area RNNs.

cs.LG↗

arXiv2023

$k$-Contraction in a Generalized Lurie System

We derive a sufficient condition for $k$-contraction in a generalized Lurie system~(GLS), that is, the feedback connection of a nonlinear dynamical system and a memoryless nonlinear function. For $k=1$, this reduces to a sufficient condition for standard contraction. For $k=2$, this condition implies that every bounded solution of the GLS converges to an equilibrium, which is not necessarily unique. We demonstrate the theoretical results by analyzing $k$-contraction in a biochemical control circuit with nonlinear dissipation terms.

eess.SY↗