9 research outputs found
Learning Task Constraints from Demonstration for Hybrid Force/Position Control
We present a novel method for learning hybrid force/position control from
demonstration. We learn a dynamic constraint frame aligned to the direction of
desired force using Cartesian Dynamic Movement Primitives. In contrast to
approaches that utilize a fixed constraint frame, our approach easily
accommodates tasks with rapidly changing task constraints over time. We
activate only one degree of freedom for force control at any given time,
ensuring motion is always possible orthogonal to the direction of desired
force. Since we utilize demonstrated forces to learn the constraint frame, we
are able to compensate for forces not detected by methods that learn only from
the demonstrated kinematic motion, such as frictional forces between the
end-effector and the contact surface. We additionally propose novel extensions
to the Dynamic Movement Primitive (DMP) framework that encourage robust
transition from free-space motion to in-contact motion in spite of environment
uncertainty. We incorporate force feedback and a dynamically shifting goal to
reduce forces applied to the environment and retain stable contact while
enabling force control. Our methods exhibit low impact forces on contact and
low steady-state tracking error.Comment: Under revie
Active Learning of Probabilistic Movement Primitives
A Probabilistic Movement Primitive (ProMP) defines a distribution over
trajectories with an associated feedback policy. ProMPs are typically
initialized from human demonstrations and achieve task generalization through
probabilistic operations. However, there is currently no principled guidance in
the literature to determine how many demonstrations a teacher should provide
and what constitutes a "good'" demonstration for promoting generalization. In
this paper, we present an active learning approach to learning a library of
ProMPs capable of task generalization over a given space. We utilize
uncertainty sampling techniques to generate a task instance for which a teacher
should provide a demonstration. The provided demonstration is incorporated into
an existing ProMP if possible, or a new ProMP is created from the demonstration
if it is determined that it is too dissimilar from existing demonstrations. We
provide a qualitative comparison between common active learning metrics;
motivated by this comparison we present a novel uncertainty sampling approach
named "Greatest Mahalanobis Distance.'' We perform grasping experiments on a
real KUKA robot and show our novel active learning measure achieves better task
generalization with fewer demonstrations than a random sampling over the space.Comment: Under revie
Latent Space Planning for Multi-Object Manipulation with Environment-Aware Relational Classifiers
Objects rarely sit in isolation in everyday human environments. If we want
robots to operate and perform tasks in our human environments, they must
understand how the objects they manipulate will interact with structural
elements of the environment for all but the simplest of tasks. As such, we'd
like our robots to reason about how multiple objects and environmental elements
relate to one another and how those relations may change as the robot interacts
with the world. We examine the problem of predicting inter-object and
object-environment relations between previously unseen objects and novel
environments purely from partial-view point clouds. Our approach enables robots
to plan and execute sequences to complete multi-object manipulation tasks
defined from logical relations. This removes the burden of providing explicit,
continuous object states as goals to the robot. We explore several different
neural network architectures for this task. We find the best performing model
to be a novel transformer-based neural network that both predicts
object-environment relations and learns a latent-space dynamics function. We
achieve reliable sim-to-real transfer without any fine-tuning. Our experiments
show that our model understands how changes in observed environmental geometry
relate to semantic relations between objects. We show more videos on our
website: https://sites.google.com/view/erelationaldynamics.Comment: Under review. Update contact information and equations in the
manuscript. arXiv admin note: substantial text overlap with arXiv:2209.1194
Occupying wide open spaces? Late Pleistocene hunter–gatherer activities in the Eastern Levant
With a specific focus on eastern Jordan, the Epipalaeolithic Foragers in Azraq Project explores changing hunter-gatherer strategies, behaviours and adaptations to this vast area throughout the Late Pleistocene. In particular, we examine how lifeways here (may have) differed from surrounding areas and what circumstances drew human and animal populations to the region. Integrating multiple material cultural and environmental datasets, we explore some of the strategies of these eastern Jordanian groups that resulted in changes in settlement, subsistence and interaction and, in some areas, the occupation of substantial aggregation sites. Five years of excavation at the aggregation site of Kharaneh IV suggest some very intriguing technological and social on-site activities, as well as adaptations to a dynamic landscape unlike that of today. Here we discuss particular aspects of the Kharaneh IV material record within the context of ongoing palaeoenvironmental reconstructions and place these findings in the wider spatial and temporal narratives of the Azraq Basin