397 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
Non-prehensile Planar Manipulation via Trajectory Optimization with Complementarity Constraints
Contact adaption is an essential capability when manipulating objects. Two
key contact modes of non-prehensile manipulation are sticking and sliding. This
paper presents a Trajectory Optimization (TO) method formulated as a
Mathematical Program with Complementarity Constraints (MPCC), which is able to
switch between these two modes. We show that this formulation can be applicable
to both planning and Model Predictive Control (MPC) for planar manipulation
tasks. We numerically compare: (i) our planner against a mixed integer
alternative, showing that the MPCC planer converges faster, scales better with
respect to time horizon, and can handle environments with obstacles; (ii) our
controller against a state-of-the-art mixed integer approach, showing that the
MPCC controller achieves better tracking and more consistent computation times.
Additionally, we experimentally validate both our planner and controller with
the KUKA LWR robot on a range of planar manipulation tasks
Unwieldy Object Delivery with Nonholonomic Mobile Base: A Stable Pushing Approach
This paper addresses the problem of pushing manipulation with nonholonomic
mobile robots. Pushing is a fundamental skill that enables robots to move
unwieldy objects that cannot be grasped. We propose a stable pushing method
that maintains stiff contact between the robot and the object to avoid
consuming repositioning actions. We prove that a line contact, rather than a
single point contact, is necessary for nonholonomic robots to achieve stable
pushing. We also show that the stable pushing constraint and the nonholonomic
constraint of the robot can be simplified as a concise linear motion
constraint. Then the pushing planning problem can be formulated as a
constrained optimization problem using nonlinear model predictive control
(NMPC). According to the experiments, our NMPC-based planner outperforms a
reactive pushing strategy in terms of efficiency, reducing the robot's traveled
distance by 23.8\% and time by 77.4\%. Furthermore, our method requires four
fewer hyperparameters and decision variables than the Linear Time-Varying (LTV)
MPC approach, making it easier to implement. Real-world experiments are carried
out to validate the proposed method with two differential-drive robots, Husky
and Boxer, under different friction conditions.Comment: The short version of the paper is accepted by RA
Demonstration-guided Optimal Control for Long-term Non-prehensile Planar Manipulation
Long-term non-prehensile planar manipulation is a challenging task for robot
planning and feedback control. It is characterized by underactuation, hybrid
control, and contact uncertainty. One main difficulty is to determine contact
points and directions, which involves joint logic and geometrical reasoning in
the modes of the dynamics model. To tackle this issue, we propose a
demonstration-guided hierarchical optimization framework to achieve offline
task and motion planning (TAMP). Our work extends the formulation of the
dynamics model of the pusher-slider system to include separation mode with face
switching cases, and solves a warm-started TAMP problem by exploiting human
demonstrations. We show that our approach can cope well with the local minima
problems currently present in the state-of-the-art solvers and determine a
valid solution to the task. We validate our results in simulation and
demonstrate its applicability on a pusher-slider system with real Franka Emika
robot in the presence of external disturbances
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
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