70 research outputs found
Goal Set Inverse Optimal Control and Iterative Re-planning for Predicting Human Reaching Motions in Shared Workspaces
To enable safe and efficient human-robot collaboration in shared workspaces
it is important for the robot to predict how a human will move when performing
a task. While predicting human motion for tasks not known a priori is very
challenging, we argue that single-arm reaching motions for known tasks in
collaborative settings (which are especially relevant for manufacturing) are
indeed predictable. Two hypotheses underlie our approach for predicting such
motions: First, that the trajectory the human performs is optimal with respect
to an unknown cost function, and second, that human adaptation to their
partner's motion can be captured well through iterative re-planning with the
above cost function. The key to our approach is thus to learn a cost function
which "explains" the motion of the human. To do this, we gather example
trajectories from pairs of participants performing a collaborative assembly
task using motion capture. We then use Inverse Optimal Control to learn a cost
function from these trajectories. Finally, we predict reaching motions from the
human's current configuration to a task-space goal region by iteratively
re-planning a trajectory using the learned cost function. Our planning
algorithm is based on the trajectory optimizer STOMP, it plans for a 23 DoF
human kinematic model and accounts for the presence of a moving collaborator
and obstacles in the environment. Our results suggest that in most cases, our
method outperforms baseline methods when predicting motions. We also show that
our method outperforms baselines for predicting human motion when a human and a
robot share the workspace.Comment: 12 pages, Accepted for publication IEEE Transaction on Robotics 201
Constrained Stein Variational Trajectory Optimization
We present Constrained Stein Variational Trajectory Optimization (CSVTO), an
algorithm for performing trajectory optimization with constraints on a set of
trajectories in parallel. We frame constrained trajectory optimization as a
novel form of constrained functional minimization over trajectory
distributions, which avoids treating the constraints as a penalty in the
objective and allows us to generate diverse sets of constraint-satisfying
trajectories. Our method uses Stein Variational Gradient Descent (SVGD) to find
a set of particles that approximates a distribution over low-cost trajectories
while obeying constraints. CSVTO is applicable to problems with arbitrary
equality and inequality constraints and includes a novel particle resampling
step to escape local minima. By explicitly generating diverse sets of
trajectories, CSVTO is better able to avoid poor local minima and is more
robust to initialization. We demonstrate that CSVTO outperforms baselines in
challenging highly-constrained tasks, such as a 7DoF wrench manipulation task,
where CSVTO succeeds in 20/20 trials vs 13/20 for the closest baseline. Our
results demonstrate that generating diverse constraint-satisfying trajectories
improves robustness to disturbances and initialization over baselines.Comment: 14 pages, 7 figure
Data Augmentation for Manipulation
The success of deep learning depends heavily on the availability of large
datasets, but in robotic manipulation there are many learning problems for
which such datasets do not exist. Collecting these datasets is time-consuming
and expensive, and therefore learning from small datasets is an important open
problem. Within computer vision, a common approach to a lack of data is data
augmentation. Data augmentation is the process of creating additional training
examples by modifying existing ones. However, because the types of tasks and
data differ, the methods used in computer vision cannot be easily adapted to
manipulation. Therefore, we propose a data augmentation method for robotic
manipulation. We argue that augmentations should be valid, relevant, and
diverse. We use these principles to formalize augmentation as an optimization
problem, with the objective function derived from physics and knowledge of the
manipulation domain. This method applies rigid body transformations to
trajectories of geometric state and action data. We test our method in two
scenarios: 1) learning the dynamics of planar pushing of rigid cylinders, and
2) learning a constraint checker for rope manipulation. These two scenarios
have different data and label types, yet in both scenarios, training on our
augmented data significantly improves performance on downstream tasks. We also
show how our augmentation method can be used on real-robot data to enable more
data-efficient online learning.Comment: Robotics Science and Systems (RSS) 2022 Project Website:
https://sites.google.com/view/data-augmentation4manipulatio
Efficient Humanoid Contact Planning using Learned Centroidal Dynamics Prediction
Humanoid robots dynamically navigate an environment by interacting with it
via contact wrenches exerted at intermittent contact poses. Therefore, it is
important to consider dynamics when planning a contact sequence. Traditional
contact planning approaches assume a quasi-static balance criterion to reduce
the computational challenges of selecting a contact sequence over a rough
terrain. This however limits the applicability of the approach when dynamic
motions are required, such as when walking down a steep slope or crossing a
wide gap. Recent methods overcome this limitation with the help of efficient
mixed integer convex programming solvers capable of synthesizing dynamic
contact sequences. Nevertheless, its exponential-time complexity limits its
applicability to short time horizon contact sequences within small
environments. In this paper, we go beyond current approaches by learning a
prediction of the dynamic evolution of the robot centroidal momenta, which can
then be used for quickly generating dynamically robust contact sequences for
robots with arms and legs using a search-based contact planner. We demonstrate
the efficiency and quality of the results of the proposed approach in a set of
dynamically challenging scenarios
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