Manipulation of Articulated Objects using Dual-arm Robots via Answer Set
Programming
- URL: http://arxiv.org/abs/2010.01164v1
- Date: Fri, 2 Oct 2020 18:50:39 GMT
- Title: Manipulation of Articulated Objects using Dual-arm Robots via Answer Set
Programming
- Authors: Riccardo Bertolucci, Alessio Capitanelli, Carmine Dodaro, Nicola
Leone, Marco Maratea, Fulvio Mastrogiovanni, Mauro Vallati
- Abstract summary: The manipulation of articulated objects is of primary importance in Robotics, and can be considered as one of the most complex manipulation tasks.
Traditionally, this problem has been tackled by developing ad-hoc approaches, which lack flexibility and portability.
We present a framework based on Answer Set Programming (ASP) for the automated manipulation of articulated objects in a robot control architecture.
- Score: 10.316694915810947
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The manipulation of articulated objects is of primary importance in Robotics,
and can be considered as one of the most complex manipulation tasks.
Traditionally, this problem has been tackled by developing ad-hoc approaches,
which lack flexibility and portability.
In this paper we present a framework based on Answer Set Programming (ASP)
for the automated manipulation of articulated objects in a robot control
architecture. In particular, ASP is employed for representing the configuration
of the articulated object, for checking the consistency of such representation
in the knowledge base, and for generating the sequence of manipulation actions.
The framework is exemplified and validated on the Baxter dual-arm manipulator
in a first, simple scenario. Then, we extend such scenario to improve the
overall setup accuracy, and to introduce a few constraints in robot actions
execution to enforce their feasibility. The extended scenario entails a high
number of possible actions that can be fruitfully combined together. Therefore,
we exploit macro actions from automated planning in order to provide more
effective plans. We validate the overall framework in the extended scenario,
thereby confirming the applicability of ASP also in more realistic Robotics
settings, and showing the usefulness of macro actions for the robot-based
manipulation of articulated objects. Under consideration in Theory and Practice
of Logic Programming (TPLP).
Related papers
- $\textbf{EMOS}$: $\textbf{E}$mbodiment-aware Heterogeneous $\textbf{M}$ulti-robot $\textbf{O}$perating $\textbf{S}$ystem with LLM Agents [33.77674812074215]
We introduce a novel multi-agent framework designed to enable effective collaboration among heterogeneous robots.
We propose a self-prompted approach, where agents comprehend robot URDF files and call robot kinematics tools to generate descriptions of their physics capabilities.
The Habitat-MAS benchmark is designed to assess how a multi-agent framework handles tasks that require embodiment-aware reasoning.
arXiv Detail & Related papers (2024-10-30T03:20:01Z) - ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation [31.211870350260703]
Keypoint Constraints (ReKep) is a visually-grounded representation for constraints in robotic manipulation.
We present system implementations on a wheeled single-arm platform and a stationary dual-arm platform.
arXiv Detail & Related papers (2024-09-03T06:45:22Z) - ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning [74.58666091522198]
We present a framework for intuitive robot programming by non-experts.
We leverage natural language prompts and contextual information from the Robot Operating System (ROS)
Our system integrates large language models (LLMs), enabling non-experts to articulate task requirements to the system through a chat interface.
arXiv Detail & Related papers (2024-06-28T08:28:38Z) - RoboCodeX: Multimodal Code Generation for Robotic Behavior Synthesis [102.1876259853457]
We propose a tree-structured multimodal code generation framework for generalized robotic behavior synthesis, termed RoboCodeX.
RoboCodeX decomposes high-level human instructions into multiple object-centric manipulation units consisting of physical preferences such as affordance and safety constraints.
To further enhance the capability to map conceptual and perceptual understanding into control commands, a specialized multimodal reasoning dataset is collected for pre-training and an iterative self-updating methodology is introduced for supervised fine-tuning.
arXiv Detail & Related papers (2024-02-25T15:31:43Z) - RoboScript: Code Generation for Free-Form Manipulation Tasks across Real
and Simulation [77.41969287400977]
This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation.
We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language.
We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
arXiv Detail & Related papers (2024-02-22T15:12:00Z) - Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions
with Large Language Model [63.66204449776262]
Instruct2Act is a framework that maps multi-modal instructions to sequential actions for robotic manipulation tasks.
Our approach is adjustable and flexible in accommodating various instruction modalities and input types.
Our zero-shot method outperformed many state-of-the-art learning-based policies in several tasks.
arXiv Detail & Related papers (2023-05-18T17:59:49Z) - Canonical mapping as a general-purpose object descriptor for robotic
manipulation [0.0]
We propose using canonical mapping as a near-universal and flexible object descriptor.
We demonstrate that common object representations can be derived from a single pre-trained canonical mapping model.
We perform a multi-stage experiment using two robot arms that demonstrate the robustness of the perception approach.
arXiv Detail & Related papers (2023-03-02T15:09:25Z) - ProgPrompt: Generating Situated Robot Task Plans using Large Language
Models [68.57918965060787]
Large language models (LLMs) can be used to score potential next actions during task planning.
We present a programmatic LLM prompt structure that enables plan generation functional across situated environments.
arXiv Detail & Related papers (2022-09-22T20:29:49Z) - V-MAO: Generative Modeling for Multi-Arm Manipulation of Articulated
Objects [51.79035249464852]
We present a framework for learning multi-arm manipulation of articulated objects.
Our framework includes a variational generative model that learns contact point distribution over object rigid parts for each robot arm.
arXiv Detail & Related papers (2021-11-07T02:31:09Z) - Behavior coordination for self-adaptive robots using constraint-based
configuration [0.0]
This paper presents an original algorithm to dynamically configure the control architecture of self-adaptive robots.
The algorithm uses a constraint-based configuration approach to decide which basic robot behaviors should be activated in response to both reactive and deliberative events.
The solution has been implemented as a software development tool called Behavior Coordinator CBC, which is based on ROS and open source.
arXiv Detail & Related papers (2021-03-24T12:09:44Z)
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.