arXiv subjects

Başak Sakçak

Publications and source records attributed to Başak Sakçak.

3 recordsLinked to original sources

arXiv2026

The Minkowski Wrap: A Relativistic Speed Limiter

In special relativity, a particle can experience constant acceleration, but at the same time, its motion is constrained by the velocity limit imposed by the speed of light $c$. Inspired by this principle, we propose a method for enforcing velocity bounds in control systems by replacing $c$ with the maximum attainable speed of the system. We refer to this as the ``Minkowski wrap," the operation of deforming the phase portrait of a system so as to enforce desired speed limits. We apply this idea to shape the input generated by a state-feedback stabilizing controller and time-optimal controller considering controlling a double integrator system. By applying Pontryagin's Maximum Principle, we show that the time-optimal control of a wrapped double-integrator system is bang-bang. The proposed method transforms the classical double-integrator dynamics into a ``wrapped" system that respects velocity bounds without the need for clipping, offering an explicit nonlinear feedback control strategy conducive to safety applications.

eess.SY↗

arXiv2026

An Information-Space Perspective to Scene Graph Sufficiency for Robotic Task Planning

Planning in complex environments requires task specifications grounded in representations that capture objects, relations, and affordances; scene graphs meet this need, but their size in large environments hinders efficient planning. While task-aware pruning and hierarchical abstractions have been explored, a general, task-centric formalization of what constitutes a sufficient scene graph for planning remains open. This paper provides such a formalization by modeling planning over scene graphs within an information-spaces framework through the definition of scene graph transition systems and relevant action semantics for navigation and manipulation. We then introduce derived scene graphs via information mappings that merge and prune nodes and induce quotient transition systems augmented with motion primitives to capture higher-level actions over merged graph nodes. Sufficiency is characterized by two conditions: (i) the information mapping yields a deterministic quotient, and (ii) the task is well-posed over derived traces, ensuring plans found on the derived model are feasible on the maximal system. We illustrate the framework using a task over an example environment, showing both sufficient and insufficient reduced scene graphs.

cs.RO↗

arXiv2026

Relating Reinforcement Learning to Dynamic Programming-Based Planning

This paper bridges some of the gap between optimal planning and reinforcement learning (RL), both of which share roots in dynamic programming applied to sequential decision making or optimal control. Whereas planning typically favors deterministic models, goal termination, and cost minimization, RL tends to favor stochastic models, infinite-horizon discounting, and reward maximization in addition to learning-related parameters such as the learning rate and greediness factor. A derandomized version of RL is developed, analyzed, and implemented to yield performance comparisons with value iteration and Dijkstra's algorithm using simple planning models. Next, mathematical analysis shows: 1) conditions under which cost minimization and reward maximization are equivalent, 2) conditions for equivalence of single-shot goal termination and infinite-horizon episodic learning, and 3) conditions under which discounting causes goal achievement to fail. The paper then advocates for defining and optimizing truecost, rather than inserting arbitrary parameters to guide operations. Performance studies are then extended to the stochastic case, using planning-oriented criteria and comparing value iteration to RL with learning rates and greediness factors.

cs.RO↗