1,486 research outputs found
PHYSICS-AWARE MODEL SIMPLIFICATION FOR INTERACTIVE VIRTUAL ENVIRONMENTS
Rigid body simulation is an integral part of Virtual Environments (VE) for autonomous planning, training, and design tasks. The underlying physics-based simulation of VE must be accurate and computationally fast enough for the intended application, which unfortunately are conflicting requirements. Two ways to perform fast and high fidelity physics-based simulation are: (1) model simplification, and (2) parallel computation. Model simplification can be used to allow simulation at an interactive rate while introducing an acceptable level of error. Currently, manual model simplification is the most common way of performing simulation speedup but it is time consuming. Hence, in order to reduce the development time of VEs, automated model simplification is needed. The dissertation presents an automated model simplification approach based on geometric reasoning, spatial decomposition, and temporal coherence. Geometric reasoning is used to develop an accessibility based algorithm for removing portions of geometric models that do not play any role in rigid body to rigid body interaction simulation. Removing such inaccessible portions of the interacting rigid body models has no influence on the simulation accuracy but reduces computation time significantly. Spatial decomposition is used to develop a clustering algorithm that reduces the number of fluid pressure computations resulting in significant speedup of rigid body and fluid interaction simulation. Temporal coherence algorithm reuses the computed force values from rigid body to fluid interaction based on the coherence of fluid surrounding the rigid body. The simulations are further sped up by performing computing on graphics processing unit (GPU). The dissertation also presents the issues pertaining to the development of parallel algorithms for rigid body simulations both on multi-core processors and GPU. The developed algorithms have enabled real-time, high fidelity, six degrees of freedom, and time domain simulation of unmanned sea surface vehicles (USSV) and can be used for autonomous motion planning, tele-operation, and learning from demonstration applications
Risk-Averse Trajectory Optimization via Sample Average Approximation
Trajectory optimization under uncertainty underpins a wide range of
applications in robotics. However, existing methods are limited in terms of
reasoning about sources of epistemic and aleatoric uncertainty, space and time
correlations, nonlinear dynamics, and non-convex constraints. In this work, we
first introduce a continuous-time planning formulation with an
average-value-at-risk constraint over the entire planning horizon. Then, we
propose a sample-based approximation that unlocks an efficient,
general-purpose, and time-consistent algorithm for risk-averse trajectory
optimization. We prove that the method is asymptotically optimal and derive
finite-sample error bounds. Simulations demonstrate the high speed and
reliability of the approach on problems with stochasticity in nonlinear
dynamics, obstacle fields, interactions, and terrain parameters
MPCGPU: Real-Time Nonlinear Model Predictive Control through Preconditioned Conjugate Gradient on the GPU
Nonlinear Model Predictive Control (NMPC) is a state-of-the-art approach for
locomotion and manipulation which leverages trajectory optimization at each
control step. While the performance of this approach is computationally
bounded, implementations of direct trajectory optimization that use iterative
methods to solve the underlying moderately-large and sparse linear systems, are
a natural fit for parallel hardware acceleration. In this work, we introduce
MPCGPU, a GPU-accelerated, real-time NMPC solver that leverages an accelerated
preconditioned conjugate gradient (PCG) linear system solver at its core. We
show that MPCGPU increases the scalability and real-time performance of NMPC,
solving larger problems, at faster rates. In particular, for tracking tasks
using the Kuka IIWA manipulator, MPCGPU is able to scale to kilohertz control
rates with trajectories as long as 512 knot points. This is driven by a custom
PCG solver which outperforms state-of-the-art, CPU-based, linear system solvers
by at least 10x for a majority of solves and 3.6x on average.Comment: Accepted to ICRA 2024, 8 pages, 6 figure
Learning to Simulate Tree-Branch Dynamics for Manipulation
We propose to use a simulation driven inverse inference approach to model the
joint dynamics of tree branches under manipulation. Learning branch dynamics
and gaining the ability to manipulate deformable vegetation can help with
occlusion-prone tasks, such as fruit picking in dense foliage, as well as
moving overhanging vines and branches for navigation in dense vegetation. The
underlying deformable tree geometry is encapsulated as coarse spring
abstractions executed on parallel, non-differentiable simulators. The implicit
statistical model defined by the simulator, reference trajectories obtained by
actively probing the ground truth, and the Bayesian formalism, together guide
the spring parameter posterior density estimation. Our non-parametric inference
algorithm, based on Stein Variational Gradient Descent, incorporates
biologically motivated assumptions into the inference process as neural network
driven learnt joint priors; moreover, it leverages the finite difference scheme
for gradient approximations. Real and simulated experiments confirm that our
model can predict deformation trajectories, quantify the estimation
uncertainty, and it can perform better when base-lined against other inference
algorithms, particularly from the Monte Carlo family. The model displays strong
robustness properties in the presence of heteroscedastic sensor noise;
furthermore, it can generalise to unseen grasp locations.Comment: 8 pages, 9 figure
DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets
Robotic grasping of 3D deformable objects is critical for real-world
applications such as food handling and robotic surgery. Unlike rigid and
articulated objects, 3D deformable objects have infinite degrees of freedom.
Fully defining their state requires 3D deformation and stress fields, which are
exceptionally difficult to analytically compute or experimentally measure.
Thus, evaluating grasp candidates for grasp planning typically requires
accurate, but slow 3D finite element method (FEM) simulation. Sampling-based
grasp planning is often impractical, as it requires evaluation of a large
number of grasp candidates. Gradient-based grasp planning can be more
efficient, but requires a differentiable model to synthesize optimal grasps
from initial candidates. Differentiable FEM simulators may fill this role, but
are typically no faster than standard FEM. In this work, we propose learning a
predictive graph neural network (GNN), DefGraspNets, to act as our
differentiable model. We train DefGraspNets to predict 3D stress and
deformation fields based on FEM-based grasp simulations. DefGraspNets not only
runs up to 1500 times faster than the FEM simulator, but also enables fast
gradient-based grasp optimization over 3D stress and deformation metrics. We
design DefGraspNets to align with real-world grasp planning practices and
demonstrate generalization across multiple test sets, including real-world
experiments.Comment: To be published in the IEEE Conference on Robotics and Automation
(ICRA), 202
Symmetric Stair Preconditioning of Linear Systems for Parallel Trajectory Optimization
There has been a growing interest in parallel strategies for solving
trajectory optimization problems. One key step in many algorithmic approaches
to trajectory optimization is the solution of moderately-large and sparse
linear systems. Iterative methods are particularly well-suited for parallel
solves of such systems. However, fast and stable convergence of iterative
methods is reliant on the application of a high-quality preconditioner that
reduces the spread and increase the clustering of the eigenvalues of the target
matrix. To improve the performance of these approaches, we present a new
parallel-friendly symmetric stair preconditioner. We prove that our
preconditioner has advantageous theoretical properties when used in conjunction
with iterative methods for trajectory optimization such as a more clustered
eigenvalue spectrum. Numerical experiments with typical trajectory optimization
problems reveal that as compared to the best alternative parallel
preconditioner from the literature, our symmetric stair preconditioner provides
up to a 34% reduction in condition number and up to a 25% reduction in the
number of resulting linear system solver iterations.Comment: Accepted to ICRA 2024, 8 pages, 3 figure
Learning to Walk and Fly with Adversarial Motion Priors
Robot multimodal locomotion encompasses the ability to transition between
walking and flying, representing a significant challenge in robotics. This work
presents an approach that enables automatic smooth transitions between legged
and aerial locomotion. Leveraging the concept of Adversarial Motion Priors, our
method allows the robot to imitate motion datasets and accomplish the desired
task without the need for complex reward functions. The robot learns walking
patterns from human-like gaits and aerial locomotion patterns from motions
obtained using trajectory optimization. Through this process, the robot adapts
the locomotion scheme based on environmental feedback using reinforcement
learning, with the spontaneous emergence of mode-switching behavior. The
results highlight the potential for achieving multimodal locomotion in aerial
humanoid robotics through automatic control of walking and flying modes, paving
the way for applications in diverse domains such as search and rescue,
surveillance, and exploration missions. This research contributes to advancing
the capabilities of aerial humanoid robots in terms of versatile locomotion in
various environments.Comment: 6 pages, 8 figures, submitted to ICRA 202
High-DOF Motion Planning in Dynamic Environments using Trajectory Optimization
Motion planning is an important problem in robotics, computer-aided design, and simulated environments. Recently, robots with a high number of controllable joints are increasingly used for different applications, including in dynamic environments with humans and other moving objects. In this thesis, we address three main challenges related to motion planning algorithms for high-DOF robots in dynamic environments: 1) how to compute a feasible and constrained motion trajectory in dynamic environments; 2) how to improve the performance of realtime computations for high-DOF robots; 3) how to model the uncertainty in the environment representation and the motion of the obstacles. We present a novel optimization-based algorithm for motion planning in dynamic environments. We model various constraints corresponding to smoothness, as well as kinematics and dynamics bounds, as a cost function, and perform stochastic trajectory optimization to compute feasible high-dimensional trajectories. In order to handle arbitrary dynamic obstacles, we use a replanning framework that interleaves planning with execution. We also parallelize our approach on multiple CPU or GPU cores to improve the performance and perform realtime computations. In order to deal with the uncertainty of dynamic environments, we present an efficient probabilistic collision detection algorithm that takes into account noisy sensor data. We predict the future obstacle motion as Gaussian distributions, and compute the bounded collision probability between a high-DOF robot and obstacles. We highlight the performance of our algorithms in simulated environments as well as with a 7-DOF Fetch arm.Doctor of Philosoph
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