A Task-Driven, Planner-in-the-Loop Computational Design Framework for Modular Manipulators

• URL: http://arxiv.org/abs/2512.16069v1
• Date: Thu, 18 Dec 2025 01:27:34 GMT
• Title: A Task-Driven, Planner-in-the-Loop Computational Design Framework for Modular Manipulators
• Abstract summary: We propose a unified task-driven computational framework that integrates trajectory planning across varying morphologies with the co-optimization of morphology and mounted pose.<n>For design optimization, the CMA-ES is employed to efficiently explore a hybrid search space consisting of discrete morphology configurations and continuous mounted poses.<n>A virtual module abstraction is introduced to enable bi-branch morphologies, allowing an auxiliary branch to offload torque from the primary branch and extend the achievable workspace.
• Score: 4.7838192146149225
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Modular manipulators composed of pre-manufactured and interchangeable modules offer high adaptability across diverse tasks. However, their deployment requires generating feasible motions while jointly optimizing morphology and mounted pose under kinematic, dynamic, and physical constraints. Moreover, traditional single-branch designs often extend reach by increasing link length, which can easily violate torque limits at the base joint. To address these challenges, we propose a unified task-driven computational framework that integrates trajectory planning across varying morphologies with the co-optimization of morphology and mounted pose. Within this framework, a hierarchical model predictive control (HMPC) strategy is developed to enable motion planning for both redundant and non-redundant manipulators. For design optimization, the CMA-ES is employed to efficiently explore a hybrid search space consisting of discrete morphology configurations and continuous mounted poses. Meanwhile, a virtual module abstraction is introduced to enable bi-branch morphologies, allowing an auxiliary branch to offload torque from the primary branch and extend the achievable workspace without increasing the capacity of individual joint modules. Extensive simulations and hardware experiments on polishing, drilling, and pick-and-place tasks demonstrate the effectiveness of the proposed framework. The results show that: 1) the framework can generate multiple feasible designs that satisfy kinematic and dynamic constraints while avoiding environmental collisions for given tasks; 2) flexible design objectives, such as maximizing manipulability, minimizing joint effort, or reducing the number of modules, can be achieved by customizing the cost functions; and 3) a bi-branch morphology capable of operating in a large workspace can be realized without requiring more powerful basic modules.

Related papers

• Generalized Task-Driven Design of Soft Robots via Reduced-Order FEM-based Surrogate Modeling [8.36237361666539]
  This paper presents a unified reduced-order finite element method (FEM)-based surrogate modeling pipeline for task-driven soft robot design.<n>High-fidelity FEM simulations characterize actuator behavior at the modular level, from which compact surrogate joint models are constructed.<n>A meta-model maps actuator design parameters to surrogate representations, enabling rapid instantiation across a parameterized actuator family.
  arXiv  Detail & Related papers  (2026-03-20T09:33:09Z)
• Design and Control of Modular Magnetic Millirobots for Multimodal Locomotion and Shape Reconfiguration [5.7788762754943654]
  We present a modular magnetic millirobotic platform for on-demand assembly and disassembly.<n>A free module supports self-assembly and reconfiguration, a fixed module enables flip-and-walk locomotion, and a gripper module for cargo manipulation.<n>The system achieves self-assembly, multimodal transformations, and disassembly at low field strengths.
  arXiv  Detail & Related papers  (2026-02-22T21:25:05Z)
• UniMotion: A Unified Motion Framework for Simulation, Prediction and Planning [60.55873455475112]
  We propose UniMotion, a unified motion framework that captures shared structures across motion tasks.<n>UniMotion employs dedicated interaction modes and tailored training strategies to simultaneously support these motion tasks.<n>With further fine-tuning, UniMotion achieves state-of-the-art performance across a range of motion tasks, establishing it as a versatile and scalable solution for autonomous driving.
  arXiv  Detail & Related papers  (2026-01-31T07:12:26Z)
• 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)
• Flexible Multitask Learning with Factorized Diffusion Policy [59.526246520933135]
  Multitask learning poses significant challenges due to the highly multimodal and diverse nature of robot action distributions.<n>Existing monolithic models often underfit the action distribution and lack the flexibility required for efficient adaptation.<n>We introduce a novel modular diffusion policy framework that factorizes complex action distributions into a composition of specialized diffusion models.
  arXiv  Detail & Related papers  (2025-12-26T07:11:47Z)
• Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generation [91.17994756436259]
  Multi-agent systems (MAS) based on large language models (LLMs) have emerged as a powerful solution for dealing with complex problems across diverse domains.<n>Existing approaches are fundamentally constrained by their reliance on a template graph modification paradigm with a predefined set of agents and hard-coded interaction structures.<n>We propose ARG-Designer, a novel autoregressive model that operationalizes this paradigm by constructing the collaboration graph from scratch.
  arXiv  Detail & Related papers  (2025-07-24T09:17:41Z)
• UniSTD: Towards Unified Spatio-Temporal Learning across Diverse Disciplines [64.84631333071728]
  We introduce bfUnistage, a unified Transformer-based framework fortemporal modeling.<n>Our work demonstrates that a task-specific vision-text can build a generalizable model fortemporal learning.<n>We also introduce a temporal module to incorporate temporal dynamics explicitly.
  arXiv  Detail & Related papers  (2025-03-26T17:33:23Z)
• Model Evolution Framework with Genetic Algorithm for Multi-Task Reinforcement Learning [85.91908329457081]
  Multi-task reinforcement learning employs a single policy to complete various tasks, aiming to develop an agent with generalizability across different scenarios.<n>Existing approaches typically use a routing network to generate specific routes for each task and reconstruct a set of modules into diverse models to complete multiple tasks simultaneously.<n>We propose a Model Evolution framework with Genetic Algorithm (MEGA), which enables the model to evolve during training according to the difficulty of the tasks.
  arXiv  Detail & Related papers  (2025-02-19T09:22:34Z)
• Optimizing Modular Robot Composition: A Lexicographic Genetic Algorithm Approach [9.471665570104802]
  The morphology, i.e., the form and structure of a robot, significantly impacts the primary performance metrics acquisition cost, cycle time, and energy efficiency. Previous approaches either lack adequate exploration of the design space or the possibility to adapt to complex tasks. We propose combining a genetic algorithm with a lexicographic evaluation of solution candidates to overcome this problem.
  arXiv  Detail & Related papers  (2023-09-15T13:50:21Z)
• An End-to-End Differentiable Framework for Contact-Aware Robot Design [37.715596272425316]
  We build an end-to-end differentiable framework for contact-aware robot design. A novel deformation-based parameterization allows for the design of articulated rigid robots with arbitrary, complex geometry. A differentiable rigid body simulator can handle contact-rich scenarios and computes analytical gradients for a full spectrum of kinematic and dynamic parameters.
  arXiv  Detail & Related papers  (2021-07-15T17:53:44Z)

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.