487 research outputs found
More than a Million Ways to Be Pushed: A High-Fidelity Experimental Dataset of Planar Pushing
Pushing is a motion primitive useful to handle objects that are too large,
too heavy, or too cluttered to be grasped. It is at the core of much of robotic
manipulation, in particular when physical interaction is involved. It seems
reasonable then to wish for robots to understand how pushed objects move.
In reality, however, robots often rely on approximations which yield models
that are computable, but also restricted and inaccurate. Just how close are
those models? How reasonable are the assumptions they are based on? To help
answer these questions, and to get a better experimental understanding of
pushing, we present a comprehensive and high-fidelity dataset of planar pushing
experiments. The dataset contains timestamped poses of a circular pusher and a
pushed object, as well as forces at the interaction.We vary the push
interaction in 6 dimensions: surface material, shape of the pushed object,
contact position, pushing direction, pushing speed, and pushing acceleration.
An industrial robot automates the data capturing along precisely controlled
position-velocity-acceleration trajectories of the pusher, which give dense
samples of positions and forces of uniform quality.
We finish the paper by characterizing the variability of friction, and
evaluating the most common assumptions and simplifications made by models of
frictional pushing in robotics.Comment: 8 pages, 10 figure
Models for pushing objects with a mobile robot using single point contact
In many mobile robotic manipulation tasks it is desirable to interact with the robots surroundings without actually grasping the object being manipulated. Non-prehensile manipulation allows a robot to interact in situations which would otherwise be impossible due to size or weight. This paper presents the derivation of a mathematical model of an object pushed by a single point and sliding in the presence of friction where the dynamic effects of mass and inertia are significant. This model is validated using numerical simulation. The derived dynamic model is also compared with a kinematic approximation from literature, showing that under certain conditions, the motion of a pushed object is similar to the motion of a non-holonomic vehicle. Finally, the results of experimental investigations are discussed and promising directions for further work are proposed. ©2010 IEEE
A Convex Polynomial Force-Motion Model for Planar Sliding: Identification and Application
We propose a polynomial force-motion model for planar sliding. The set of
generalized friction loads is the 1-sublevel set of a polynomial whose gradient
directions correspond to generalized velocities. Additionally, the polynomial
is confined to be convex even-degree homogeneous in order to obey the maximum
work inequality, symmetry, shape invariance in scale, and fast invertibility.
We present a simple and statistically-efficient model identification procedure
using a sum-of-squares convex relaxation. Simulation and robotic experiments
validate the accuracy and efficiency of our approach. We also show practical
applications of our model including stable pushing of objects and free sliding
dynamic simulations.Comment: 2016 IEEE International Conference on Robotics and Automation (ICRA
Orienting polyhedral parts by pushing
A common task in automated manufacturing processes is to orient parts prior to assembly. We consider sensorless orientation of an asymmetric polyhedral part by a sequence of push actions, and show that is it possible to move any such part from an unknown initial orientation into a known final orientation if these actions are performed by a jaw consisting of two orthogonal planes. We also show how to compute an orienting sequence of push actions.We propose a three-dimensional generalization of conveyor belts with fences consisting of a sequence of tilted plates with curved tips; each of the plates contains a sequence of fences. We show that it is possible to compute a set-up of plates and fences for any given asymmetric polyhedral part such that the part gets oriented on its descent along plates and fences
Sim-to-Real Transfer of Robotic Control with Dynamics Randomization
Simulations are attractive environments for training agents as they provide
an abundant source of data and alleviate certain safety concerns during the
training process. But the behaviours developed by agents in simulation are
often specific to the characteristics of the simulator. Due to modeling error,
strategies that are successful in simulation may not transfer to their real
world counterparts. In this paper, we demonstrate a simple method to bridge
this "reality gap". By randomizing the dynamics of the simulator during
training, we are able to develop policies that are capable of adapting to very
different dynamics, including ones that differ significantly from the dynamics
on which the policies were trained. This adaptivity enables the policies to
generalize to the dynamics of the real world without any training on the
physical system. Our approach is demonstrated on an object pushing task using a
robotic arm. Despite being trained exclusively in simulation, our policies are
able to maintain a similar level of performance when deployed on a real robot,
reliably moving an object to a desired location from random initial
configurations. We explore the impact of various design decisions and show that
the resulting policies are robust to significant calibration error
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
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