A Robust Task-Level Control Architecture for Learned Dynamical Systems

• URL: http://arxiv.org/abs/2511.09790v1
• Date: Fri, 14 Nov 2025 01:10:00 GMT
• Title: A Robust Task-Level Control Architecture for Learned Dynamical Systems
• Authors: Eshika Pathak, Ahmed Aboudonia, Sandeep Banik, Naira Hovakimyan,
• Abstract summary: Dynamical system (DS)-based learning from demonstration (LfD) is a powerful tool for generating motion plans in the operation space of robotic systems.<n>We propose a novel task-level robust control architecture, L1-augmented Dynamical Systems (L1-DS)<n>Our framework augments any DS-based LfD model with a nominal stabilizing controller and an L1 adaptive controller.
• Score: 5.9875599887427216
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dynamical system (DS)-based learning from demonstration (LfD) is a powerful tool for generating motion plans in the operation (`task') space of robotic systems. However, the realization of the generated motion plans is often compromised by a ''task-execution mismatch'', where unmodeled dynamics, persistent disturbances, and system latency cause the robot's actual task-space state to diverge from the desired motion trajectory. We propose a novel task-level robust control architecture, L1-augmented Dynamical Systems (L1-DS), that explicitly handles the task-execution mismatch in tracking a nominal motion plan generated by any DS-based LfD scheme. Our framework augments any DS-based LfD model with a nominal stabilizing controller and an L1 adaptive controller. Furthermore, we introduce a windowed Dynamic Time Warping (DTW)-based target selector, which enables the nominal stabilizing controller to handle temporal misalignment for improved phase-consistent tracking. We demonstrate the efficacy of our architecture on the LASA and IROS handwriting datasets.

Related papers

• LLM-Grounded Dynamic Task Planning with Hierarchical Temporal Logic for Human-Aware Multi-Robot Collaboration [17.886091169216538]
  Large Language Models (LLM) enable non-experts to specify openworld multi-robot tasks.<n>LLM plans often lack feasibility and are not efficient, especially in long-horizon scenarios.<n>We propose a neuro-symbolic framework that grounds reasoning into hierarchical specifications.
  arXiv  Detail & Related papers  (2026-02-10T07:11:36Z)
• SP-VLA: A Joint Model Scheduling and Token Pruning Approach for VLA Model Acceleration [70.72227437717467]
  Vision-Language-Action (VLA) models have attracted increasing attention for their strong control capabilities.<n>Their high computational cost and low execution frequency hinder their suitability for real-time tasks such as robotic manipulation and autonomous navigation.<n>We propose SP-VLA, a unified framework that accelerates VLA models by jointly scheduling models and pruning tokens.
  arXiv  Detail & Related papers  (2025-06-15T05:04:17Z)
• Dynamic Manipulation of Deformable Objects in 3D: Simulation, Benchmark and Learning Strategy [88.8665000676562]
  Prior methods often simplify the problem to low-speed or 2D settings, limiting their applicability to real-world 3D tasks.<n>To mitigate data scarcity, we introduce a novel simulation framework and benchmark grounded in reduced-order dynamics.<n>We propose Dynamics Informed Diffusion Policy (DIDP), a framework that integrates imitation pretraining with physics-informed test-time adaptation.
  arXiv  Detail & Related papers  (2025-05-23T03:28:25Z)
• Zero-Shot Trajectory Planning for Signal Temporal Logic Tasks [7.389002274709231]
  Signal Temporal Logic (STL) is a powerful specification language for describing complex temporal behaviors of continuous signals.<n> generating executable STL plans for STL tasks is challenging, as it requires consideration of the coupling between the task specification and the system dynamics.<n>We propose a hierarchical planning framework that enables zero-shot generalization to new STL tasks by leveraging only task-agnostic trajectory data during offline training.
  arXiv  Detail & Related papers  (2025-01-23T08:15:52Z)
• Monte Carlo Tree Search with Velocity Obstacles for safe and efficient motion planning in dynamic environments [49.30744329170107]
  We propose a novel approach for optimal online motion planning with minimal information about dynamic obstacles.<n>The proposed methodology combines Monte Carlo Tree Search (MCTS), for online optimal planning via model simulations, with Velocity Obstacles (VO), for obstacle avoidance.<n>We show the superiority of our methodology with respect to state-of-the-art planners, including Non-linear Model Predictive Control (NMPC), in terms of improved collision rate, computational and task performance.
  arXiv  Detail & Related papers  (2025-01-16T16:45:08Z)
• RobotDiffuse: Motion Planning for Redundant Manipulator based on Diffusion Model [13.110235244912474]
  Redundant manipulators offer enhanced kinematic performance and versatility.<n>Motion planning for these manipulators is challenging due to increased DOFs and complex, dynamic environments.<n>This paper introduces RobotDiffuse, a diffusion model-based approach for motion planning in redundant manipulators.
  arXiv  Detail & Related papers  (2024-12-27T07:34:54Z)
• Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics [68.36528819227641]
  This paper systematically evaluates the robustness of Vision-Language-Action (VLA) models.<n>We introduce two untargeted attack objectives that leverage spatial foundations to destabilize robotic actions, and a targeted attack objective that manipulates the robotic trajectory.<n>We design an adversarial patch generation approach that places a small, colorful patch within the camera's view, effectively executing the attack in both digital and physical environments.
  arXiv  Detail & Related papers  (2024-11-18T01:52:20Z)
• Iterative Learning Control of Fast, Nonlinear, Oscillatory Dynamics (Preprint) [0.0]
  nonlinear, chaotic, and are often too fast for active control schemes. We develop an alternative active controls system using an iterative, trajectory-optimization and parameter-tuning approach. We demonstrate that the controller is robust to missing information and uncontrollable parameters as long as certain requirements are met.
  arXiv  Detail & Related papers  (2024-05-30T13:27:17Z)
• Interactive Character Control with Auto-Regressive Motion Diffusion Models [18.727066177880708]
  We propose A-MDM (Auto-regressive Motion Diffusion Model) for real-time motion synthesis. Our conditional diffusion model takes an initial pose as input, and auto-regressively generates successive motion frames conditioned on previous frame. We introduce a suite of techniques for incorporating interactive controls into A-MDM, such as task-oriented sampling, in-painting, and hierarchical reinforcement learning.
  arXiv  Detail & Related papers  (2023-06-01T07:48:34Z)
• OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation [50.59541802645156]
  Operational Space Control (OSC) has been used as an effective task-space controller for manipulation. We propose OSC for Adaptation and Robustness (OSCAR), a data-driven variant of OSC that compensates for modeling errors. We evaluate our method on a variety of simulated manipulation problems, and find substantial improvements over an array of controller baselines.
  arXiv  Detail & Related papers  (2021-10-02T01:21:38Z)
• Imitation Learning for Robust and Safe Real-time Motion Planning: A Contraction Theory Approach [9.35511513240868]
  LAG-ROS is a real-time robust motion planning algorithm for safety-critical nonlinear systems perturbed by bounded disturbances. The LAG-ROS achieves higher control performance and task success rate with faster execution speed for real-time computation.
  arXiv  Detail & Related papers  (2021-02-25T03:47:15Z)
• Online Reinforcement Learning Control by Direct Heuristic Dynamic Programming: from Time-Driven to Event-Driven [80.94390916562179]
  Time-driven learning refers to the machine learning method that updates parameters in a prediction model continuously as new data arrives. It is desirable to prevent the time-driven dHDP from updating due to insignificant system event such as noise. We show how the event-driven dHDP algorithm works in comparison to the original time-driven dHDP.
  arXiv  Detail & Related papers  (2020-06-16T05:51:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.