13 research outputs found
Reactive Planar Manipulation with Convex Hybrid MPC
This paper presents a reactive controller for planar manipulation tasks that
leverages machine learning to achieve real-time performance. The approach is
based on a Model Predictive Control (MPC) formulation, where the goal is to
find an optimal sequence of robot motions to achieve a desired object motion.
Due to the multiple contact modes associated with frictional interactions, the
resulting optimization program suffers from combinatorial complexity when
tasked with determining the optimal sequence of modes.
To overcome this difficulty, we formulate the search for the optimal mode
sequences offline, separately from the search for optimal control inputs
online. Using tools from machine learning, this leads to a convex hybrid MPC
program that can be solved in real-time. We validate our algorithm on a planar
manipulation experimental setup where results show that the convex hybrid MPC
formulation with learned modes achieves good closed-loop performance on a
trajectory tracking problem
Contact Optimization for Non-Prehensile Loco-Manipulation via Hierarchical Model Predictive Control
Recent studies on quadruped robots have focused on either locomotion or
mobile manipulation using a robotic arm. Legged robots can manipulate heavier
and larger objects using non-prehensile manipulation primitives, such as planar
pushing, to drive the object to the desired location. In this paper, we present
a novel hierarchical model predictive control (MPC) for contact optimization of
the manipulation task. Using two cascading MPCs, we split the loco-manipulation
problem into two parts: the first to optimize both contact force and contact
location between the robot and the object, and the second to regulate the
desired interaction force through the robot locomotion. Our method is
successfully validated in both simulation and hardware experiments. While the
baseline locomotion MPC fails to follow the desired trajectory of the object,
our proposed approach can effectively control both object's position and
orientation with minimal tracking error. This capability also allows us to
perform obstacle avoidance for both the robot and the object during the
loco-manipulation task.Comment: 7 pages, 9 figure
Linear Time-Varying MPC for Nonprehensile Object Manipulation with a Nonholonomic Mobile Robot
This paper proposes a technique to manipulate an object with a nonholonomic
mobile robot by pushing, which is a nonprehensile manipulation motion
primitive. Such a primitive involves unilateral constraints associated with the
friction between the robot and the manipulated object. Violating this
constraint produces the slippage of the object during the manipulation,
preventing the correct achievement of the task. A linear time-varying model
predictive control is designed to include the unilateral constraint within the
control action properly. The approach is verified in a dynamic simulation
environment through a Pioneer 3-DX wheeled robot executing the pushing
manipulation of a package
Rearrangement-Based Manipulation via Kinodynamic Planning and Dynamic Planning Horizons
Robot manipulation in cluttered environments often requires complex and
sequential rearrangement of multiple objects in order to achieve the desired
reconfiguration of the target objects. Due to the sophisticated physical
interactions involved in such scenarios, rearrangement-based manipulation is
still limited to a small range of tasks and is especially vulnerable to
physical uncertainties and perception noise. This paper presents a planning
framework that leverages the efficiency of sampling-based planning approaches,
and closes the manipulation loop by dynamically controlling the planning
horizon. Our approach interleaves planning and execution to progressively
approach the manipulation goal while correcting any errors or path deviations
along the process. Meanwhile, our framework allows the definition of
manipulation goals without requiring explicit goal configurations, enabling the
robot to flexibly interact with all objects to facilitate the manipulation of
the target ones. With extensive experiments both in simulation and on a real
robot, we evaluate our framework on three manipulation tasks in cluttered
environments: grasping, relocating, and sorting. In comparison with two
baseline approaches, we show that our framework can significantly improve
planning efficiency, robustness against physical uncertainties, and task
success rate under limited time budgets.Comment: Accepted for publication in the Proceedings of the 2022 IEEE/RSJ
International Conference on Intelligent Robots and Systems (IROS 2022
Nonprehensile Dynamic Manipulation: A Survey
Nonprehensile dynamic manipulation can be reason- ably considered as the most complex manipulation task. It might be argued that such a task is still rather far from being fully solved and applied in robotics. This survey tries to collect the results reached so far by the research community about planning and control in the nonprehensile dynamic manipulation domain. A discussion about current open issues is addressed as well
Non-Prehensile Object Transportation via Model Predictive Non-Sliding Manipulation Control
This article proposes a model predictive non-sliding manipulation (MPNSM) control approach to safely transport an object on a tray-like end-effector of a robotic manipulator. For the considered non-prehensile transportation task to succeed, both non-sliding manipulation and the robotic system constraints must always be satisfied. To tackle this problem, we devise a model predictive controller enforcing sticking contacts, i.e., preventing sliding between the object and the tray, and assuring that physical limits such as extreme joint positions, velocities, and input torques are never exceeded. The combined dynamic model of the physical system, comprising the manipulator and the object in contact, is derived in a compact form. The associated non-sliding manipulation constraint is formulated such that the parametrized contact forces belong to a conservatively approximated friction cone space. This constraint is enforced by the proposed MPNSM controller, formulated as an optimal control problem that optimizes the objective of tracking the desired trajectory while always satisfying both manipulation and robotic system constraints. We validate our approach by showing extensive dynamic simulations using a torque-controlled 7-degree-of-freedom (DoF) KUKA LBR IIWA robotic manipulator. Finally, demonstrative results from real experiments conducted on a 21-DoF humanoid robotic platform are shown
A Shared-Control Teleoperation Architecture for Nonprehensile Object Transportation
This article proposes a shared-control teleoperation architecture for robot manipulators transporting an object on a tray. Differently from many existing studies about remotely operated robots with firm grasping capabilities, we consider the case in which, in principle, the object can break its contact with the robot end-effector. The proposed shared-control approach automatically regulates the remote robot motion commanded by the user and the end-effector orientation to prevent the object from sliding over the tray. Furthermore, the human operator is provided with haptic cues informing about the discrepancy between the commanded and executed robot motion, which assist the operator throughout the task execution. We carried out trajectory tracking experiments employing an autonomous 7-degree-of-freedom (DoF) manipulator and compared the results obtained using the proposed approach with two different control schemes (i.e., constant tray orientation and no motion adjustment). We also carried out a human-subjects study involving 18 participants in which a 3-DoF haptic device was used to teleoperate the robot linear motion and display haptic cues to the operator. In all experiments, the results clearly show that our control approach outperforms the other solutions in terms of sliding prevention, robustness, commands tracking, and user’s preference