79,840 research outputs found
Experimental Validation of Contact Dynamics for In-Hand Manipulation
This paper evaluates state-of-the-art contact models at predicting the
motions and forces involved in simple in-hand robotic manipulations. In
particular it focuses on three primitive actions --linear sliding, pivoting,
and rolling-- that involve contacts between a gripper, a rigid object, and
their environment. The evaluation is done through thousands of controlled
experiments designed to capture the motion of object and gripper, and all
contact forces and torques at 250Hz. We demonstrate that a contact modeling
approach based on Coulomb's friction law and maximum energy principle is
effective at reasoning about interaction to first order, but limited for making
accurate predictions. We attribute the major limitations to 1) the
non-uniqueness of force resolution inherent to grasps with multiple hard
contacts of complex geometries, 2) unmodeled dynamics due to contact
compliance, and 3) unmodeled geometries dueto manufacturing defects.Comment: International Symposium on Experimental Robotics, ISER 2016, Tokyo,
Japa
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
A probabilistic data-driven model for planar pushing
This paper presents a data-driven approach to model planar pushing
interaction to predict both the most likely outcome of a push and its expected
variability. The learned models rely on a variation of Gaussian processes with
input-dependent noise called Variational Heteroscedastic Gaussian processes
(VHGP) that capture the mean and variance of a stochastic function. We show
that we can learn accurate models that outperform analytical models after less
than 100 samples and saturate in performance with less than 1000 samples. We
validate the results against a collected dataset of repeated trajectories, and
use the learned models to study questions such as the nature of the variability
in pushing, and the validity of the quasi-static assumption.Comment: 8 pages, 11 figures, ICRA 201
Stable Prehensile Pushing: In-Hand Manipulation with Alternating Sticking Contacts
This paper presents an approach to in-hand manipulation planning that
exploits the mechanics of alternating sticking contact. Particularly, we
consider the problem of manipulating a grasped object using external pushes for
which the pusher sticks to the object. Given the physical properties of the
object, frictional coefficients at contacts and a desired regrasp on the
object, we propose a sampling-based planning framework that builds a pushing
strategy concatenating different feasible stable pushes to achieve the desired
regrasp. An efficient dynamics formulation allows us to plan in-hand
manipulations 100-1000 times faster than our previous work which builds upon a
complementarity formulation. Experimental observations for the generated plans
show that the object precisely moves in the grasp as expected by the planner.
Video Summary -- youtu.be/qOTKRJMx6HoComment: IEEE International Conference on Robotics and Automation 201
Propagation Networks for Model-Based Control Under Partial Observation
There has been an increasing interest in learning dynamics simulators for
model-based control. Compared with off-the-shelf physics engines, a learnable
simulator can quickly adapt to unseen objects, scenes, and tasks. However,
existing models like interaction networks only work for fully observable
systems; they also only consider pairwise interactions within a single time
step, both restricting their use in practical systems. We introduce Propagation
Networks (PropNet), a differentiable, learnable dynamics model that handles
partially observable scenarios and enables instantaneous propagation of signals
beyond pairwise interactions. Experiments show that our propagation networks
not only outperform current learnable physics engines in forward simulation,
but also achieve superior performance on various control tasks. Compared with
existing model-free deep reinforcement learning algorithms, model-based control
with propagation networks is more accurate, efficient, and generalizable to
new, partially observable scenes and tasks.Comment: Accepted to ICRA 2019. Project Page: http://propnet.csail.mit.edu
Video: https://youtu.be/ZAxHXegkz4
Learning Latent Space Dynamics for Tactile Servoing
To achieve a dexterous robotic manipulation, we need to endow our robot with
tactile feedback capability, i.e. the ability to drive action based on tactile
sensing. In this paper, we specifically address the challenge of tactile
servoing, i.e. given the current tactile sensing and a target/goal tactile
sensing --memorized from a successful task execution in the past-- what is the
action that will bring the current tactile sensing to move closer towards the
target tactile sensing at the next time step. We develop a data-driven approach
to acquire a dynamics model for tactile servoing by learning from
demonstration. Moreover, our method represents the tactile sensing information
as to lie on a surface --or a 2D manifold-- and perform a manifold learning,
making it applicable to any tactile skin geometry. We evaluate our method on a
contact point tracking task using a robot equipped with a tactile finger. A
video demonstrating our approach can be seen in https://youtu.be/0QK0-Vx7WkIComment: Accepted to be published at the International Conference on Robotics
and Automation (ICRA) 2019. The final version for publication at ICRA 2019 is
7 pages (i.e. 6 pages of technical content (including text, figures, tables,
acknowledgement, etc.) and 1 page of the Bibliography/References), while this
arXiv version is 8 pages (added Appendix and some extra details
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