95 research outputs found
Persistent Homology Guided Monte-Carlo Tree Search for Effective Non-Prehensile Manipulation
Performing object retrieval tasks in messy real-world workspaces involves the
challenges of \emph{uncertainty} and \emph{clutter}. One option is to solve
retrieval problems via a sequence of prehensile pick-n-place operations, which
can be computationally expensive to compute in highly-cluttered scenarios and
also inefficient to execute. The proposed framework selects the option of
performing non-prehensile actions, such as pushing, to clean a cluttered
workspace to allow a robotic arm to retrieve a target object. Non-prehensile
actions, allow to interact simultaneously with multiple objects, which can
speed up execution. At the same time, they can significantly increase
uncertainty as it is not easy to accurately estimate the outcome of a pushing
operation in clutter. The proposed framework integrates topological tools and
Monte-Carlo tree search to achieve effective and robust pushing for object
retrieval problems. In particular, it proposes using persistent homology to
automatically identify manageable clustering of blocking objects in the
workspace without the need for manually adjusting hyper-parameters.
Furthermore, MCTS uses this information to explore feasible actions to push
groups of objects together, aiming to minimize the number of pushing actions
needed to clear the path to the target object. Real-world experiments using a
Baxter robot, which involves some noise in actuation, show that the proposed
framework achieves a higher success rate in solving retrieval tasks in dense
clutter compared to state-of-the-art alternatives. Moreover, it produces
high-quality solutions with a small number of pushing actions improving the
overall execution time. More critically, it is robust enough that it allows to
plan the sequence of actions offline and then execute them reliably online with
Baxter
ORLA*: Mobile Manipulator-Based Object Rearrangement with Lazy A*
Effectively performing object rearrangement is an essential skill for mobile
manipulators, e.g., setting up a dinner table or organizing a desk. A key
challenge in such problems is deciding an appropriate manipulation order for
objects to effectively untangle dependencies between objects while considering
the necessary motions for realizing the manipulations (e.g., pick and place).
To our knowledge, computing time-optimal multi-object rearrangement solutions
for mobile manipulators remains a largely untapped research direction. In this
research, we propose ORLA*, which leverages delayed (lazy) evaluation in
searching for a high-quality object pick and place sequence that considers both
end-effector and mobile robot base travel. ORLA* also supports multi-layered
rearrangement tasks considering pile stability using machine learning.
Employing an optimal solver for finding temporary locations for displacing
objects, ORLA* can achieve global optimality. Through extensive simulation and
ablation study, we confirm the effectiveness of ORLA* delivering quality
solutions for challenging rearrangement instances. Supplementary materials are
available at: https://gaokai15.github.io/ORLA-Star/Comment: Submitted to ICRA 202
Online Replanning With Human-in-the-Loop for Non-Prehensile Manipulation in Clutter — A Trajectory Optimization Based Approach
We are interested in the problem where a number of robots, in parallel, are trying to solve reaching through clutter problems in a simulated warehouse setting. In such a setting, we investigate the performance increase that can be achieved by using a human-in-the-loop providing guidance to robot planners. These manipulation problems are challenging for autonomous planners as they have to search for a solution in a high-dimensional space. In addition, physics simulators suffer from the uncertainty problem where a valid trajectory in simulation can be invalid when executing the trajectory in the real-world. To tackle these problems, we propose an online-replanning method with a human-in-the-loop. This system enables a robot to plan and execute a trajectory autonomously, but also to seek high-level suggestions from a human operator if required at any point during execution. This method aims to minimize the human effort required, thereby increasing the number of robots that can be guided in parallel by a single human operator. We performed experiments in simulation and on a real robot, using an experienced and a novice operator. Our results show a significant increase in performance when using our approach in a simulated warehouse scenario and six robots
Manipulation Planning Among Movable Obstacles Using Physics-Based Adaptive Motion Primitives
Robot manipulation in cluttered scenes often requires contact-rich
interactions with objects. It can be more economical to interact via
non-prehensile actions, for example, push through other objects to get to the
desired grasp pose, instead of deliberate prehensile rearrangement of the
scene. For each object in a scene, depending on its properties, the robot may
or may not be allowed to make contact with, tilt, or topple it. To ensure that
these constraints are satisfied during non-prehensile interactions, a planner
can query a physics-based simulator to evaluate the complex multi-body
interactions caused by robot actions. Unfortunately, it is infeasible to query
the simulator for thousands of actions that need to be evaluated in a typical
planning problem as each simulation is time-consuming. In this work, we show
that (i) manipulation tasks (specifically pick-and-place style tasks from a
tabletop or a refrigerator) can often be solved by restricting robot-object
interactions to adaptive motion primitives in a plan, (ii) these actions can be
incorporated as subgoals within a multi-heuristic search framework, and (iii)
limiting interactions to these actions can help reduce the time spent querying
the simulator during planning by up to 40x in comparison to baseline
algorithms. Our algorithm is evaluated in simulation and in the real-world on a
PR2 robot using PyBullet as our physics-based simulator. Supplementary video:
\url{https://youtu.be/ABQc7JbeJPM}.Comment: Under review for the IEEE Robotics and Automation Letters (RA-L)
journal with conference presentation option at the 2021 International
Conference on Robotics and Automation (ICRA). This work has been submitted to
the IEEE for possible publication. Copyright may be transferred without
notice, after which this version may no longer be accessibl
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