Taxonomy and Modular Tool System for Versatile and Effective Non-Prehensile Manipulations
- URL: http://arxiv.org/abs/2512.11080v1
- Date: Thu, 11 Dec 2025 19:52:40 GMT
- Title: Taxonomy and Modular Tool System for Versatile and Effective Non-Prehensile Manipulations
- Abstract summary: We discuss a taxonomy for the key properties of a non-actuated end-effector.<n>We present a modular tool system that can be used by a standard two-fingered gripper to extend its versatility.
- Score: 5.7178303574016285
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: General-purpose robotic end-effectors of limited complexity, like the parallel-jaw gripper, are appealing for their balance of simplicity and effectiveness in a wide range of manipulation tasks. However, while many such manipulators offer versatility in grasp-like interactions, they are not optimized for non-prehensile actions like pressing, rubbing, or scraping -- manipulations needed for many common tasks. To perform such tasks, humans use a range of different body parts or tools with different rigidity, friction, etc., according to the properties most effective for a given task. Here, we discuss a taxonomy for the key properties of a non-actuated end-effector, laying the groundwork for a systematic understanding of the affordances of non-prehensile manipulators. We then present a modular tool system, based on the taxonomy, that can be used by a standard two-fingered gripper to extend its versatility and effectiveness in performing such actions. We demonstrate the application of the tool system in aerospace and household scenarios that require a range of non-prehensile and prehensile manipulations.
Related papers
- ARTiS: An Adaptive Robotic Gripper for Enhanced Tool Manipulation in Disassembly Applications [6.66666011357805]
ARTiS (Adaptive Robotic Tool Gripper in Disassembly Systems) is a novel gripper that combines the adaptability of soft grippers, the dexterity of anthropomorphic hands, and the robustness of rigid mechanisms with a soft palm and fingertips.<n>A comprehensive evaluation was conducted using a range of conventional disassembly tools to assess the gripper's compliance, durability, and functional versatility.
arXiv Detail & Related papers (2026-09-03T04:38:22Z) - Mana: Dexterous Manipulation of Articulated Tools [64.16038120159688]
Mana is a general sim-to-real framework that reinterprets dexterous manipulation as an animation problem.<n>Inspired by computer animation, Mana employs a coarse-to-fine pipeline that transforms procedurally-generated grasps into manipulation trajectories.<n>Mana achieves zero-shot sim-to-real transfer for both grasping and in-hand manipulation, demonstrating a scalable approach to dexterous articulated tool use.
arXiv Detail & Related papers (2026-06-11T17:59:49Z) - Any-ttach: Quick End-effector Swapping Enables Manipulation Dexterity with Simplicity [15.00126597335838]
Any-ttach is a tool-centric manipulation framework that treats quick end-effector swapping as a mechanism for dexterity with simplicity.<n>The system supports diverse tools and end-effector modules, including daily tools, articulated tools such as scissors, Fin Ray fingers, and a low-cost anthropomorphic hand.<n>Our experiments show that Any-ttach improves tool-swapping reliability, increases demonstration efficiency, reduces tool-pose variability, and supports diverse tool-use skills.
arXiv Detail & Related papers (2026-05-28T21:00:52Z) - AdaReasoner: Dynamic Tool Orchestration for Iterative Visual Reasoning [66.24374176797075]
We introduce textbfAdaReasoner, a family of multimodal models that learn tool use as a general reasoning skill rather than as tool-specific or explicitly supervised behavior.<n>AdaReasoner is enabled by (i) a scalable data curation pipeline exposing models to long-horizon, multi-step tool interactions; (ii) Tool-GRPO, a reinforcement learning algorithm that prioritizes tool selection and sequencing based on end-task success; and (iii) an adaptive learning mechanism that dynamically regulates tool usage.
arXiv Detail & Related papers (2026-01-26T16:04:43Z) - Adaptive Inverse Kinematics Framework for Learning Variable-Length Tool Manipulation in Robotics [0.0]
Conventional robots possess a limited understanding of their kinematics and are confined to preprogrammed tasks.<n>We introduce a pioneering framework to expand the capabilities of the robot's inverse kinematics solver.<n>Our model achieves virtually indistinguishable performance when employing two distinct tools of different lengths.
arXiv Detail & Related papers (2025-10-30T14:44:24Z) - ImaginationPolicy: Towards Generalizable, Precise and Reliable End-to-End Policy for Robotic Manipulation [46.06124092071133]
We propose a novel Chain of Moving Oriented Keypoints (CoMOK) formulation for robotic manipulation.<n>Our formulation is used as the action representation of a neural policy, which can be trained in an end-to-end fashion.
arXiv Detail & Related papers (2025-09-25T07:29:07Z) - CAIMAN: Causal Action Influence Detection for Sample-efficient Loco-manipulation [17.94272840532448]
We present CAIMAN, a reinforcement learning framework that encourages robots to gain control over other entities in the environment.<n>We empirically demonstrate CAIMAN's superior sample efficiency and adaptability to diverse scenarios in simulation.
arXiv Detail & Related papers (2025-02-02T16:16:53Z) - Twisting Lids Off with Two Hands [82.21668778600414]
We show how policies trained in simulation can be effectively and efficiently transferred to the real world.
Specifically, we consider the problem of twisting lids of various bottle-like objects with two hands.
This is the first sim-to-real RL system that enables such capabilities on bimanual multi-fingered hands.
arXiv Detail & Related papers (2024-03-04T18:59:30Z) - Learning Reusable Manipulation Strategies [86.07442931141634]
Humans demonstrate an impressive ability to acquire and generalize manipulation "tricks"
We present a framework that enables machines to acquire such manipulation skills through a single demonstration and self-play.
These learned mechanisms and samplers can be seamlessly integrated into standard task and motion planners.
arXiv Detail & Related papers (2023-11-06T17:35:42Z) - Dexterous Manipulation from Images: Autonomous Real-World RL via Substep
Guidance [71.36749876465618]
We describe a system for vision-based dexterous manipulation that provides a "programming-free" approach for users to define new tasks.
Our system includes a framework for users to define a final task and intermediate sub-tasks with image examples.
experimental results with a four-finger robotic hand learning multi-stage object manipulation tasks directly in the real world.
arXiv Detail & Related papers (2022-12-19T22:50:40Z) - CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action
Spaces [9.578169216444813]
This paper proposes an approach to coordinating multi-robot manipulation through learned latent action spaces that are shared across different agents.
We validate our method in simulated multi-robot manipulation tasks and demonstrate improvement over previous baselines in terms of sample efficiency and learning performance.
arXiv Detail & Related papers (2022-11-28T23:20:47Z) - Vision-driven Compliant Manipulation for Reliable, High-Precision
Assembly Tasks [26.445959214209505]
This paper demonstrates that the combination of state-of-the-art object tracking with passively adaptive mechanical hardware can be leveraged to complete precision manipulation tasks.
The proposed control method closes the loop through vision by tracking the relative 6D pose of objects in the relevant workspace.
arXiv Detail & Related papers (2021-06-26T17:54:16Z)
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.