348 research outputs found
Dynamic Inference on Graphs using Structured Transition Models
Enabling robots to perform complex dynamic tasks such as picking up an object
in one sweeping motion or pushing off a wall to quickly turn a corner is a
challenging problem. The dynamic interactions implicit in these tasks are
critical towards the successful execution of such tasks. Graph neural networks
(GNNs) provide a principled way of learning the dynamics of interactive systems
but can suffer from scaling issues as the number of interactions increases.
Furthermore, the problem of using learned GNN-based models for optimal control
is insufficiently explored. In this work, we present a method for efficiently
learning the dynamics of interacting systems by simultaneously learning a
dynamic graph structure and a stable and locally linear forward model of the
system. The dynamic graph structure encodes evolving contact modes along a
trajectory by making probabilistic predictions over the edges of the graph.
Additionally, we introduce a temporal dependence in the learned graph structure
which allows us to incorporate contact measurement updates during execution
thus enabling more accurate forward predictions. The learned stable and locally
linear dynamics enable the use of optimal control algorithms such as iLQR for
long-horizon planning and control for complex interactive tasks. Through
experiments in simulation and in the real world, we evaluate the performance of
our method by using the learned interaction dynamics for control and
demonstrate generalization to more objects and interactions not seen during
training. We introduce a control scheme that takes advantage of contact
measurement updates and hence is robust to prediction inaccuracies during
execution
Estimating Material Properties of Interacting Objects Using Sum-GP-UCB
Robots need to estimate the material and dynamic properties of objects from
observations in order to simulate them accurately. We present a Bayesian
optimization approach to identifying the material property parameters of
objects based on a set of observations. Our focus is on estimating these
properties based on observations of scenes with different sets of interacting
objects. We propose an approach that exploits the structure of the reward
function by modeling the reward for each observation separately and using only
the parameters of the objects in that scene as inputs. The resulting
lower-dimensional models generalize better over the parameter space, which in
turn results in a faster optimization. To speed up the optimization process
further, and reduce the number of simulation runs needed to find good parameter
values, we also propose partial evaluations of the reward function, wherein the
selected parameters are only evaluated on a subset of real world evaluations.
The approach was successfully evaluated on a set of scenes with a wide range of
object interactions, and we showed that our method can effectively perform
incremental learning without resetting the rewards of the gathered
observations
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