7,957 research outputs found
Contact-Implicit Trajectory Optimization Based on a Variable Smooth Contact Model and Successive Convexification
In this paper, we propose a contact-implicit trajectory optimization (CITO)
method based on a variable smooth contact model (VSCM) and successive
convexification (SCvx). The VSCM facilitates the convergence of gradient-based
optimization without compromising physical fidelity. On the other hand, the
proposed SCvx-based approach combines the advantages of direct and shooting
methods for CITO. For evaluations, we consider non-prehensile manipulation
tasks. The proposed method is compared to a version based on iterative linear
quadratic regulator (iLQR) on a planar example. The results demonstrate that
both methods can find physically-consistent motions that complete the tasks
without a meaningful initial guess owing to the VSCM. The proposed SCvx-based
method outperforms the iLQR-based method in terms of convergence, computation
time, and the quality of motions found. Finally, the proposed SCvx-based method
is tested on a standard robot platform and shown to perform efficiently for a
real-world application.Comment: Accepted for publication in ICRA 201
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
Contact-Implicit Trajectory Optimization using an Analytically Solvable Contact Model for Locomotion on Variable Ground
This paper presents a novel contact-implicit trajectory optimization method
using an analytically solvable contact model to enable planning of interactions
with hard, soft, and slippery environments. Specifically, we propose a novel
contact model that can be computed in closed-form, satisfies friction cone
constraints and can be embedded into direct trajectory optimization frameworks
without complementarity constraints. The closed-form solution decouples the
computation of the contact forces from other actuation forces and this property
is used to formulate a minimal direct optimization problem expressed with
configuration variables only. Our simulation study demonstrates the advantages
over the rigid contact model and a trajectory optimization approach based on
complementarity constraints. The proposed model enables physics-based
optimization for a wide range of interactions with hard, slippery, and soft
grounds in a unified manner expressed by two parameters only. By computing
trotting and jumping motions for a quadruped robot, the proposed optimization
demonstrates the versatility for multi-contact motion planning on surfaces with
different physical properties.Comment: in IEEE Robotics and Automation Letter
Material Recognition CNNs and Hierarchical Planning for Biped Robot Locomotion on Slippery Terrain
In this paper we tackle the problem of visually predicting surface friction
for environments with diverse surfaces, and integrating this knowledge into
biped robot locomotion planning. The problem is essential for autonomous robot
locomotion since diverse surfaces with varying friction abound in the real
world, from wood to ceramic tiles, grass or ice, which may cause difficulties
or huge energy costs for robot locomotion if not considered. We propose to
estimate friction and its uncertainty from visual estimation of material
classes using convolutional neural networks, together with probability
distribution functions of friction associated with each material. We then
robustly integrate the friction predictions into a hierarchical (footstep and
full-body) planning method using chance constraints, and optimize the same
trajectory costs at both levels of the planning method for consistency. Our
solution achieves fully autonomous perception and locomotion on slippery
terrain, which considers not only friction and its uncertainty, but also
collision, stability and trajectory cost. We show promising friction prediction
results in real pictures of outdoor scenarios, and planning experiments on a
real robot facing surfaces with different friction
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