46 research outputs found
Decentralized Adaptive Control for Collaborative Manipulation of Rigid Bodies
In this work, we consider a group of robots working together to manipulate a
rigid object to track a desired trajectory in . The robots do not know
the mass or friction properties of the object, or where they are attached to
the object. They can, however, access a common state measurement, either from
one robot broadcasting its measurements to the team, or by all robots
communicating and averaging their state measurements to estimate the state of
their centroid. To solve this problem, we propose a decentralized adaptive
control scheme wherein each agent maintains and adapts its own estimate of the
object parameters in order to track a reference trajectory. We present an
analysis of the controller's behavior, and show that all closed-loop signals
remain bounded, and that the system trajectory will almost always (except for
initial conditions on a set of measure zero) converge to the desired
trajectory. We study the proposed controller's performance using numerical
simulations of a manipulation task in 3D, as well as hardware experiments which
demonstrate our algorithm on a planar manipulation task. These studies, taken
together, demonstrate the effectiveness of the proposed controller even in the
presence of numerous unmodeled effects, such as discretization errors and
complex frictional interactions
FRoGGeR: Fast Robust Grasp Generation via the Min-Weight Metric
Many approaches to grasp synthesis optimize analytic quality metrics that
measure grasp robustness based on finger placements and local surface geometry.
However, generating feasible dexterous grasps by optimizing these metrics is
slow, often taking minutes. To address this issue, this paper presents FRoGGeR:
a method that quickly generates robust precision grasps using the min-weight
metric, a novel, almost-everywhere differentiable approximation of the
classical epsilon grasp metric. The min-weight metric is simple and
interpretable, provides a reasonable measure of grasp robustness, and admits
numerically efficient gradients for smooth optimization. We leverage these
properties to rapidly synthesize collision-free robust grasps - typically in
less than a second. FRoGGeR can refine the candidate grasps generated by other
methods (heuristic, data-driven, etc.) and is compatible with many object
representations (SDFs, meshes, etc.). We study FRoGGeR's performance on over 40
objects drawn from the YCB dataset, outperforming a competitive baseline in
computation time, feasibility rate of grasp synthesis, and picking success in
simulation. We conclude that FRoGGeR is fast: it has a median synthesis time of
0.834s over hundreds of experiments.Comment: Accepted at IROS 2023. The arXiv version contains the appendix, which
does not appear in the conference versio
Generative Modeling of Residuals for Real-Time Risk-Sensitive Safety with Discrete-Time Control Barrier Functions
A key source of brittleness for robotic systems is the presence of model
uncertainty and external disturbances. Most existing approaches to robust
control either seek to bound the worst-case disturbance (which results in
conservative behavior), or to learn a deterministic dynamics model (which is
unable to capture uncertain dynamics or disturbances). This work proposes a
different approach: training a state-conditioned generative model to represent
the distribution of error residuals between the nominal dynamics and the actual
system. In particular we introduce the Online Risk-Informed Optimization
controller (ORIO), which uses Discrete-Time Control Barrier Functions, combined
with a learned, generative disturbance model, to ensure the safety of the
system up to some level of risk. We demonstrate our approach in both
simulations and hardware, and show our method can learn a disturbance model
that is accurate enough to enable risk-sensitive control of a quadrotor flying
aggressively with an unmodelled slung load. We use a conditional variational
autoencoder (CVAE) to learn a state-conditioned dynamics residual distribution,
and find that the resulting probabilistic safety controller, which can be run
at 100Hz on an embedded computer, exhibits less conservative behavior while
retaining theoretical safety properties.Comment: 9 pages, 6 figures, submitted to the 2024 IEEE International
Conference on Robotics and Automation (ICRA 2024
Decentralized Adaptive Control for Collaborative Manipulation of Rigid Bodies
In this work, we consider a group of robots working together to manipulate a rigid object to track a desired trajectory in SE(3) . The robots do not know the mass or friction properties of the object, or where they are attached to the object. They can, however, access a common state measurement, either from one robot broadcasting its measurements to the team, or by all robots communicating and averaging their state measurements to estimate the state of their centroid. To solve this problem, we propose a decentralized adaptive control scheme wherein each agent maintains and adapts its own estimate of the object parameters in order to track a reference trajectory. We present an analysis of the controller’s behavior, and show that all closed-loop signals remain bounded, and that the system trajectory will almost always (except for initial conditions on a set of measure zero) converge to the desired trajectory. We study the proposed controller’s performance using numerical simulations of a manipulation task in 3-D, as well as hardware experiments which demonstrate our algorithm on a planar manipulation task. These studies, taken together, demonstrate the effectiveness of the proposed controller even in the presence of numerous unmodeled effects, such as discretization errors and complex frictional interactions
Learning Mixed-Integer Convex Optimization Strategies for Robot Planning and Control
© 2020 IEEE. Mixed-integer convex programming (MICP) has seen significant algorithmic and hardware improvements with several orders of magnitude solve time speedups compared to 25 years ago. Despite these advances, MICP has been rarely applied to real-world robotic control because the solution times are still too slow for online applications. In this work, we present the CoCo (Combinatorial Offline, Convex Online) framework to solve MICPs arising in robotics at very high speed. CoCo encodes the combinatorial part of the optimal solution into a strategy. Using data collected from offline problem solutions, we train a multiclass classifier to predict the optimal strategy given problem-specific parameters such as states or obstacles. Compared to [1], we use task-specific strategies and prune redundant ones to significantly reduce the number of classes the predictor has to select from, thereby greatly improving scalability. Given the predicted strategy, the control task becomes a small convex optimization problem that we can solve in milliseconds. Numerical experiments on a cart-pole system with walls, a free-flying space robot, and task-oriented grasps show that our method provides not only 1 to 2 orders of magnitude speedups compared to state-of-the-art solvers but also performance close to the globally optimal MICP solution