arXiv subjects

Ali Umut Kaypak

Publications and source records attributed to Ali Umut Kaypak.

7 recordsLinked to original sources

arXiv2026

H\"older Signed Distance: A Differentiable, Signed, Parallelizable Metric for Robotics

Computing distances between sets is essential in robotic motion planning and control, where differentiable gradients enable real-time optimization. The Euclidean Signed Distance Function (SDF), however, is not differentiable everywhere, and existing alternatives often sacrifice differentiability, sign information, or computational efficiency. In this letter, we introduce a novel differentiable signed distance between convex polyhedra. To this end, we first propose differentiable versions of the minimum and maximum operators, termed the H\"older minimum and H\"older maximum. We then replace the original min-max operators in the classical SDF formulation, yielding the H\"older signed distance. Unlike prior differentiable distance formulations that rely on iterative algorithms, our approach is computed in closed form, eliminating convergence issues while remaining naturally amenable to GPU parallelization. We validate the practical advantages and computational performance of the proposed distance through runtime comparisons with existing approaches. We also present a robotic manipulator experiment, demonstrating its suitability for applications in control.

cs.RO↗

arXiv2026

A Control Barrier Function-Constrained Model Predictive Control Framework for Safe Reinforcement Learning

Ensuring safety under unknown and stochastic dynamics remains a significant challenge in reinforcement learning (RL). In this paper, we propose a model predictive control (MPC)-based safe RL framework, called Probabilistic Ensembles with CBF-constrained Trajectory Sampling (PECTS), to address this challenge. PECTS jointly learns stochastic system dynamics with probabilistic neural networks (PNNs) and control barrier functions (CBFs) with Lipschitz-bounded neural networks. Safety is enforced by incorporating learned CBF constraints into the MPC formulation while accounting for the model stochasticity. This enables probabilistic safety under model uncertainty. To solve the resulting MPC problem, we utilize a sampling-based optimizer together with a safe trajectory sampling method that discards unsafe trajectories based on the learned system model and CBF. We validate PECTS in various simulation studies, where it outperforms baseline methods.

eess.SY↗

arXiv2026

RESCORE: LLM-Driven Simulation Recovery in Control Systems Research Papers

Reconstructing numerical simulations from control systems research papers is often hindered by underspecified parameters and ambiguous implementation details. We define the task of Paper to Simulation Recoverability, the ability of an automated system to generate executable code that faithfully reproduces a paper's results. We curate a benchmark of 500 papers from the IEEE Conference on Decision and Control (CDC) and propose RESCORE, a three component LLM agentic framework, Analyzer, Coder, and Verifier. RESCORE uses iterative execution feedback and visual comparison to improve reconstruction fidelity. Our method successfully recovers task coherent simulations for 40.7% of benchmark instances, outperforming single pass generation. Notably, the RESCORE automated pipeline achieves an estimated 10X speedup over manual human replication, drastically cutting the time and effort required to verify published control methodologies. We will release our benchmark and agents to foster community progress in automated research replication.

cs.AI↗

arXiv2025

Safe Multi-Robotic Arm Interaction via 3D Convex Shapes

Inter-robot collisions pose a significant safety risk when multiple robotic arms operate in close proximity. We present an online collision avoidance methodology leveraging High-Order Control Barrier Functions (HOCBFs) constructed for safe interactions among 3D convex shapes to address this issue. While prior works focused on using Control Barrier Functions (CBFs) for human-robotic arm and single-arm collision avoidance, we explore the problem of collision avoidance between multiple robotic arms operating in a shared space. In our methodology, we utilize the proposed HOCBFs as centralized and decentralized safety filters. These safety filters are compatible with many nominal controllers and ensure safety without significantly restricting the robots' workspace. A key challenge in implementing these filters is the computational overhead caused by the large number of safety constraints and the computation of a Hessian matrix per constraint. We address this challenge by employing numerical differentiation methods to approximate computationally intensive terms. The effectiveness of our method is demonstrated through extensive simulation studies and real-world experiments with Franka Research 3 robotic arms. The project video is available at this link.

cs.RO↗

arXiv2024

Open-Architecture End-to-End System for Real-World Autonomous Robot Navigation

Enabling robots to autonomously navigate unknown, complex, and dynamic real-world environments presents several challenges, including imperfect perception, partial observability, localization uncertainty, and safety constraints. Current approaches are typically limited to simulations, where such challenges are not present. In this work, we present a lightweight, open-architecture, end-to-end system for real-world robot autonomous navigation. Specifically, we deploy a real-time navigation system on a quadruped robot by integrating multiple onboard components that communicate via ROS2. Given navigation tasks specified in natural language, the system fuses onboard sensory data for localization and mapping with open-vocabulary semantics to build hierarchical scene graphs from a continuously updated semantic object map. An LLM-based planner leverages these graphs to generate and adapt multi-step plans in real time as the scene evolves. Through experiments across multiple indoor environments using a Unitree Go2 quadruped, we demonstrate zero-shot real-world autonomous navigation, achieving over 88% task success, and provide analysis of system behavior during deployment.

cs.RO↗

arXiv2024

MultiTalk: Introspective and Extrospective Dialogue for Human-Environment-LLM Alignment

LLMs have shown promising results in task planning due to their strong natural language understanding and reasoning capabilities. However, issues such as hallucinations, ambiguities in human instructions, environmental constraints, and limitations in the executing agent's capabilities often lead to flawed or incomplete plans. This paper proposes MultiTalk, an LLM-based task planning methodology that addresses these issues through a framework of introspective and extrospective dialogue loops. This approach helps ground generated plans in the context of the environment and the agent's capabilities, while also resolving uncertainties and ambiguities in the given task. These loops are enabled by specialized systems designed to extract and predict task-specific states, and flag mismatches or misalignments among the human user, the LLM agent, and the environment. Effective feedback pathways between these systems and the LLM planner foster meaningful dialogue. The efficacy of this methodology is demonstrated through its application to robotic manipulation tasks. Experiments and ablations highlight the robustness and reliability of our method, and comparisons with baselines further illustrate the superiority of MultiTalk in task planning for embodied agents.

cs.RO↗

arXiv2024

Grounding LLMs For Robot Task Planning Using Closed-loop State Feedback

Planning algorithms decompose complex problems into intermediate steps that can be sequentially executed by robots to complete tasks. Recent works have employed Large Language Models (LLMs) for task planning, using natural language to generate robot policies in both simulation and real-world environments. LLMs like GPT-4 have shown promising results in generalizing to unseen tasks, but their applicability is limited due to hallucinations caused by insufficient grounding in the robot environment. The robustness of LLMs in task planning can be enhanced with environmental state information and feedback. In this paper, we introduce a novel approach to task planning that utilizes two separate LLMs for high-level planning and low-level control, improving task-related success rates and goal condition recall. Our algorithm, \textit{BrainBody-LLM}, draws inspiration from the human neural system, emulating its brain-body architecture by dividing planning across two LLMs in a structured, hierarchical manner. BrainBody-LLM implements a closed-loop feedback mechanism, enabling learning from simulator errors to resolve execution errors in complex settings. We demonstrate the successful application of BrainBody-LLM in the VirtualHome simulation environment, achieving a 29\% improvement in task-oriented success rates over competitive baselines with the GPT-4 backend. Additionally, we evaluate our algorithm on seven complex tasks using a realistic physics simulator and the Franka Research 3 robotic arm, comparing it with various state-of-the-art LLMs. Our results show advancements in the reasoning capabilities of recent LLMs, which enable them to learn from raw simulator/controller errors to correct plans, making them highly effective in robotic task planning.

cs.RO↗