796 research outputs found
Optimization Model for Planning Precision Grasps with Multi-Fingered Hands
Precision grasps with multi-fingered hands are important for precise
placement and in-hand manipulation tasks. Searching precision grasps on the
object represented by point cloud, is challenging due to the complex object
shape, high-dimensionality, collision and undesired properties of the sensing
and positioning. This paper proposes an optimization model to search for
precision grasps with multi-fingered hands. The model takes noisy point cloud
of the object as input and optimizes the grasp quality by iteratively searching
for the palm pose and finger joints positions. The collision between the hand
and the object is approximated and penalized by a series of least-squares. The
collision approximation is able to handle the point cloud representation of the
objects with complex shapes. The proposed optimization model is able to locate
collision-free optimal precision grasps efficiently. The average computation
time is 0.50 sec/grasp. The searching is robust to the incompleteness and noise
of the point cloud. The effectiveness of the algorithm is demonstrated by
experiments.Comment: Submitted to IROS2019, experiment on BarrettHand, 8 page
Contact Models in Robotics: a Comparative Analysis
Physics simulation is ubiquitous in robotics. Whether in model-based
approaches (e.g., trajectory optimization), or model-free algorithms (e.g.,
reinforcement learning), physics simulators are a central component of modern
control pipelines in robotics. Over the past decades, several robotic
simulators have been developed, each with dedicated contact modeling
assumptions and algorithmic solutions. In this article, we survey the main
contact models and the associated numerical methods commonly used in robotics
for simulating advanced robot motions involving contact interactions. In
particular, we recall the physical laws underlying contacts and friction (i.e.,
Signorini condition, Coulomb's law, and the maximum dissipation principle), and
how they are transcribed in current simulators. For each physics engine, we
expose their inherent physical relaxations along with their limitations due to
the numerical techniques employed. Based on our study, we propose theoretically
grounded quantitative criteria on which we build benchmarks assessing both the
physical and computational aspects of simulation. We support our work with an
open-source and efficient C++ implementation of the existing algorithmic
variations. Our results demonstrate that some approximations or algorithms
commonly used in robotics can severely widen the reality gap and impact target
applications. We hope this work will help motivate the development of new
contact models, contact solvers, and robotic simulators in general, at the root
of recent progress in motion generation in robotics
A learning approach to the FOM problem
Hogan recently provided an heuristic technique called family of modes (FOM) to solve model predictive control (MPC) problems under hybrid constraints and underactuation. The goal of this study is to further develop this new method and to expand its usage in the robotics manipulation community. With that objective in mind, we address some of the method's weaknesses, we provide comparison tools to try to compare the method with traditional MPC solving techniques and we provide a simple and systematic technique to set-up the method's parameters. We conclude the study by presenting our the future lines of research, which consist in generalizing the method for more complex systems and testing it's robustness.Outgoin
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