1,211 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
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
ADD: Analytically Differentiable Dynamics for Multi-Body Systems with Frictional Contact
We present a differentiable dynamics solver that is able to handle frictional
contact for rigid and deformable objects within a unified framework. Through a
principled mollification of normal and tangential contact forces, our method
circumvents the main difficulties inherent to the non-smooth nature of
frictional contact. We combine this new contact model with fully-implicit time
integration to obtain a robust and efficient dynamics solver that is
analytically differentiable. In conjunction with adjoint sensitivity analysis,
our formulation enables gradient-based optimization with adaptive trade-offs
between simulation accuracy and smoothness of objective function landscapes. We
thoroughly analyse our approach on a set of simulation examples involving rigid
bodies, visco-elastic materials, and coupled multi-body systems. We furthermore
showcase applications of our differentiable simulator to parameter estimation
for deformable objects, motion planning for robotic manipulation, trajectory
optimization for compliant walking robots, as well as efficient self-supervised
learning of control policies.Comment: Moritz Geilinger and David Hahn contributed equally to this wor
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