Plug, Play, and Comply: A Modular Framework for Online Variable Impedance with Arbitrarily Oriented Compliance Axes
- URL: http://arxiv.org/abs/2607.22483v1
- Date: Fri, 24 Jul 2026 16:53:51 GMT
- Title: Plug, Play, and Comply: A Modular Framework for Online Variable Impedance with Arbitrarily Oriented Compliance Axes
- Abstract summary: The paper proposes a robot-agnostic compliant-control framework that extends the ROS control ecosystem.<n>A plugin-based architecture separates controller infrastructure from control-law implementation.<n>The complete framework, including reference controllers, high-level task interfaces, and example configurations for various manipulators, is open-sourced.
- Score: 0.16114012813668935
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: The paper proposes a robot-agnostic compliant-control framework that extends the ROS control ecosystem with standardized joint and Cartesian command interfaces. It addresses a key limitation of existing control software: no reusable infrastructure for implementing compliant-control algorithms across different manipulators while preserving a common interface to higher-level applications. A plugin-based architecture separates controller infrastructure from control-law implementation. Generic wrappers use existing hardware abstractions to interface with different manipulators, while runtime-loaded plugins implement only the control law. Command interfaces support joint- and Cartesian-space references, stiffness and damping gains, nullspace targets, and feedforward terms, enabling variable impedance and diverse compliant-control formulations. Robot kinematics and dynamics are computed from URDF models using Pinocchio. The architecture facilitates the development of compliant-control strategies and enables the same implementation to be deployed across platforms unchanged. The complete framework, including reference controllers, high-level task interfaces, and example configurations for various manipulators, is open-sourced. The reference Cartesian impedance controller supports task-dependent compliance by rotating translational and rotational stiffness and damping, allowing the principal compliance directions to be updated online according to local task geometry rather than remaining fixed in the robot base or TCP frame. This is particularly important in contact-rich manipulation, where the desired directions of motion, constraints, and compliance directions may vary throughout task execution. Real-robot experiments demonstrate task-dependent compliance in contact-rich manipulation, while simulations show portability across manipulators with distinct kinematic and dynamic characteristics.
Related papers
- A Continual Learning Framework for Adaptive Control of Modular Soft Robots [4.374184567811783]
Soft robots have attracted significant attention in applications such as medical intervention, rehabilitation, and robotic manipulation.<n>MSRs represent an emerging class of robotic systems with highly deformable and reconfigurable structures capable of performing complex tasks.<n>Existing approaches typically require controllers to be retrained from scratch whenever the robot morphology changes.<n>We propose a continual learning inspired control framework capable of incrementally adapting to changes in robot morphology.
arXiv Detail & Related papers (2026-07-07T19:11:07Z) - CoStream: Composing Simple Behaviors for Generalizable Complex Manipulation [61.25264898597319]
We show that complex manipulation capabilities can emerge naturally from the composition of simple, independent behaviors.<n>We propose CoStream, a framework orchestrating foundation models and diverse sensing modalities into multiple composable core behaviors.<n>We demonstrate CoStream on 8 real-world tasks spanning everyday manipulation and precision assembly, with the strongest gains in contact-rich assembly and object transfer.
arXiv Detail & Related papers (2026-06-24T22:25:19Z) - CEER: Compliant End-Effector and Root Control as a Unified Interface for Hierarchical Humanoid Loco-Manipulation [11.545606851036787]
We propose a compliant end-effector-root (EE-root) control abstraction for modular humanoid loco-manipulation.<n>EE-root provides a practical abstraction for humanoid loco-manipulation, enabling modular and scalable integration of diverse skills.
arXiv Detail & Related papers (2026-05-19T15:23:40Z) - Learning Reactive Dexterous Grasping via Hierarchical Task-Space RL Planning and Joint-Space QP Control [50.28263951510334]
We propose a hybrid hierarchical control framework for reactive dexterous grasping.<n>The proposed framework explicitly decouples high-level spatial intent from low-level joint execution.<n>We extensively validate the proposed framework through a rigorous simulation-to-reality pipeline.
arXiv Detail & Related papers (2026-05-05T04:49:38Z) - Small Model as Master Orchestrator: Learning Unified Agent-Tool Orchestration with Parallel Subtask Decomposition [61.291733522717415]
Agent-as-Tool is a unified parallel orchestration paradigm that mitigates both agents and tools into a standardized, learnable action space.<n>ParaManager decouples planning decisions from subtask solving, enabling state-aware parallel subtask decomposition, delegation, and asynchronous execution.<n>Experiments show that ParaManager achieves strong performance across multiple benchmarks and exhibits robust generalization under unseen model pools.
arXiv Detail & Related papers (2026-04-18T14:41:27Z) - Real-Time Projected Adaptive Control for Closed-Chain Co-Manipulative Continuum Robots [0.764671395172401]
In co-manipulative continuum robots ( CCRs), multiple continuum arms cooperate by grasping a common flexible object.<n>In practical tasks, the flexible object's physical parameters are often unknown and vary between operations.<n>This paper presents a projected adaptive control framework for CCRs formulated at the dynamic level.
arXiv Detail & Related papers (2026-04-05T22:03:23Z) - REGAL: A Registry-Driven Architecture for Deterministic Grounding of Agentic AI in Enterprise Telemetry [0.0]
Large Language Models (LLMs) enable new forms of agentic automation.<n>We present REGAL, a registry-driven architecture for deterministic grounding of agentic AI systems in enterprise telemetry.
arXiv Detail & Related papers (2026-03-03T14:13:39Z) - A Unified Experimental Architecture for Informative Path Planning: from Simulation to Deployment with GuadalPlanner [69.43049144653882]
This paper introduces a unified architecture that decouples high-level decision-making from vehicle-specific control.<n>The proposed architecture is realized through GuadalPlanner, which defines standardized interfaces between planning, sensing, and vehicle execution.
arXiv Detail & Related papers (2026-02-11T10:02:31Z) - AR-MOT: Autoregressive Multi-object Tracking [56.09738000988466]
We propose a novel autoregressive paradigm that formulates MOT as a sequence generation task within a large language model (LLM) framework.<n>This design enables the model to output structured results through flexible sequence construction, without requiring any task-specific heads.<n>To enhance region-level visual perception, we introduce an Object Tokenizer based on a pretrained detector.
arXiv Detail & Related papers (2026-01-05T09:17:28Z) - EasySteer: A Unified Framework for High-Performance and Extensible LLM Steering [55.56674028743782]
Large language model (LLM) steering has emerged as a promising paradigm for controlling model behavior at inference time.<n>We present EasySteer, a unified framework for high-performance, LLM steering built on vLLM.
arXiv Detail & Related papers (2025-09-29T17:59:07Z) - Optimal Controller Realizations against False Data Injections in Cooperative Driving [2.2134894590368748]
We study a controller-oriented approach to mitigate the effect of a class of False-Data Injection (FDI) attacks.
We show that a class of new but equivalent controllers can represent the base controller.
We obtain the optimal combination of sensors that minimizes the effect of FDI attacks.
arXiv Detail & Related papers (2024-04-08T09:53:42Z)
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.