59,431 research outputs found
Active model learning and diverse action sampling for task and motion planning
The objective of this work is to augment the basic abilities of a robot by
learning to use new sensorimotor primitives to enable the solution of complex
long-horizon problems. Solving long-horizon problems in complex domains
requires flexible generative planning that can combine primitive abilities in
novel combinations to solve problems as they arise in the world. In order to
plan to combine primitive actions, we must have models of the preconditions and
effects of those actions: under what circumstances will executing this
primitive achieve some particular effect in the world?
We use, and develop novel improvements on, state-of-the-art methods for
active learning and sampling. We use Gaussian process methods for learning the
conditions of operator effectiveness from small numbers of expensive training
examples collected by experimentation on a robot. We develop adaptive sampling
methods for generating diverse elements of continuous sets (such as robot
configurations and object poses) during planning for solving a new task, so
that planning is as efficient as possible. We demonstrate these methods in an
integrated system, combining newly learned models with an efficient
continuous-space robot task and motion planner to learn to solve long horizon
problems more efficiently than was previously possible.Comment: Proceedings of the 2018 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS), Madrid, Spain.
https://www.youtube.com/playlist?list=PLoWhBFPMfSzDbc8CYelsbHZa1d3uz-W_
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
Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation
Manipulation of deformable objects, such as ropes and cloth, is an important
but challenging problem in robotics. We present a learning-based system where a
robot takes as input a sequence of images of a human manipulating a rope from
an initial to goal configuration, and outputs a sequence of actions that can
reproduce the human demonstration, using only monocular images as input. To
perform this task, the robot learns a pixel-level inverse dynamics model of
rope manipulation directly from images in a self-supervised manner, using about
60K interactions with the rope collected autonomously by the robot. The human
demonstration provides a high-level plan of what to do and the low-level
inverse model is used to execute the plan. We show that by combining the high
and low-level plans, the robot can successfully manipulate a rope into a
variety of target shapes using only a sequence of human-provided images for
direction.Comment: 8 pages, accepted to International Conference on Robotics and
Automation (ICRA) 201
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