Human-Guided Planner for Non-Prehensile Manipulation

04/02/2020
by   Rafael Papallas, et al.
0

We present a human-guided planner for non-prehensile manipulation in clutter. Most recent approaches to manipulation in clutter employs randomized planning, however, the problem remains a challenging one where the planning times are still in the order of tens of seconds or minutes, and the success rates are low for difficult instances of the problem. We build on these control-based randomized planning approaches, but we investigate using them in conjunction with human-operator input. We show that with a minimal amount of human input, the low-level planner can solve the problem faster and with higher success rates.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 3

04/07/2019

Non-Prehensile Manipulation in Clutter with Human-In-The-Loop

We propose a human-operator guided planning approach to pushing-based ro...
04/19/2018

Preference-Guided Planning: An Active Elicitation Approach

Planning with preferences has been employed extensively to quickly gener...
11/27/2017

Randomized Physics-based Motion Planning for Grasping in Cluttered and Uncertain Environments

Planning motions to grasp an object in cluttered and uncertain environme...
04/17/2022

Robust Task Planning for Assembly Lines with Human-Robot Collaboration

Efficient and robust task planning for a human-robot collaboration (HRC)...
02/27/2013

Epsilon-Safe Planning

We introduce an approach to high-level conditional planning we call epsi...
01/29/2020

Learning When to Trust a Dynamics Model for Planning in Reduced State Spaces

When the dynamics of a system are difficult to model and/or time-consumi...
04/05/2019

Pixels to Plans: Learning Non-Prehensile Manipulation by Imitating a Planner

We present a novel method enabling robots to quickly learn to manipulate...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.