[2512.00939] Constant-Time Motion Planning with Manipulation Behaviors

[2512.00939] Constant-Time Motion Planning with Manipulation Behaviors

arXiv - AI 4 min read

About this article

Abstract page for arXiv paper 2512.00939: Constant-Time Motion Planning with Manipulation Behaviors

Computer Science > Robotics arXiv:2512.00939 (cs) [Submitted on 30 Nov 2025 (v1), last revised 26 Mar 2026 (this version, v2)] Title:Constant-Time Motion Planning with Manipulation Behaviors Authors:Nayesha Gandotra, Itamar Mishani, Maxim Likhachev View a PDF of the paper titled Constant-Time Motion Planning with Manipulation Behaviors, by Nayesha Gandotra and 2 other authors View PDF HTML (experimental) Abstract:Recent progress in contact-rich robotic manipulation has been striking, yet most deployed systems remain confined to simple, scripted routines. One of the key barriers is the lack of motion planning algorithms that can provide verifiable guarantees for safety, efficiency and reliability. To address this, a family of algorithms called Constant-Time Motion Planning (CTMP) was introduced, which leverages a preprocessing phase to enable collision-free motion queries in a fixed, user-specified time budget (e.g., 10 milliseconds). However, existing CTMP methods do not explicitly incorporate the manipulation behaviors essential for object handling. To bridge this gap, we introduce the \textit{Behavioral Constant-Time Motion Planner} (B-CTMP), an algorithm that extends CTMP to solve a broad class of two-step manipulation tasks: (1) a collision-free motion to a behavior initiation state, followed by (2) execution of a manipulation behavior (such as grasping or insertion) to reach the goal. By precomputing compact data structures, B-CTMP guarantees constant-time query in me...

Originally published on March 27, 2026. Curated by AI News.

Related Articles

Machine Learning

[D] Looking for definition of open-world ish learning problem

Hello! Recently I did a project where I initially had around 30 target classes. But at inference, the model had to be able to handle a lo...

Reddit - Machine Learning · 1 min ·
[2603.11687] SemBench: A Universal Semantic Framework for LLM Evaluation
Llms

[2603.11687] SemBench: A Universal Semantic Framework for LLM Evaluation

Abstract page for arXiv paper 2603.11687: SemBench: A Universal Semantic Framework for LLM Evaluation

arXiv - AI · 4 min ·
[2603.11583] UtilityMax Prompting: A Formal Framework for Multi-Objective Large Language Model Optimization
Llms

[2603.11583] UtilityMax Prompting: A Formal Framework for Multi-Objective Large Language Model Optimization

Abstract page for arXiv paper 2603.11583: UtilityMax Prompting: A Formal Framework for Multi-Objective Large Language Model Optimization

arXiv - AI · 3 min ·
[2512.05245] STAR-GO: Improving Protein Function Prediction by Learning to Hierarchically Integrate Ontology-Informed Semantic Embeddings
Machine Learning

[2512.05245] STAR-GO: Improving Protein Function Prediction by Learning to Hierarchically Integrate Ontology-Informed Semantic Embeddings

Abstract page for arXiv paper 2512.05245: STAR-GO: Improving Protein Function Prediction by Learning to Hierarchically Integrate Ontology...

arXiv - Machine Learning · 4 min ·
More in Nlp: This Week Guide Trending

No comments

No comments yet. Be the first to comment!

Stay updated with AI News

Get the latest news, tools, and insights delivered to your inbox.

Daily or weekly digest • Unsubscribe anytime