20,949 research outputs found
Object-Oriented Dynamics Learning through Multi-Level Abstraction
Object-based approaches for learning action-conditioned dynamics has
demonstrated promise for generalization and interpretability. However, existing
approaches suffer from structural limitations and optimization difficulties for
common environments with multiple dynamic objects. In this paper, we present a
novel self-supervised learning framework, called Multi-level Abstraction
Object-oriented Predictor (MAOP), which employs a three-level learning
architecture that enables efficient object-based dynamics learning from raw
visual observations. We also design a spatial-temporal relational reasoning
mechanism for MAOP to support instance-level dynamics learning and handle
partial observability. Our results show that MAOP significantly outperforms
previous methods in terms of sample efficiency and generalization over novel
environments for learning environment models. We also demonstrate that learned
dynamics models enable efficient planning in unseen environments, comparable to
true environment models. In addition, MAOP learns semantically and visually
interpretable disentangled representations.Comment: Accepted to the Thirthy-Fourth AAAI Conference On Artificial
Intelligence (AAAI), 202
Learning Symbolic Operators for Task and Motion Planning
Robotic planning problems in hybrid state and action spaces can be solved by
integrated task and motion planners (TAMP) that handle the complex interaction
between motion-level decisions and task-level plan feasibility. TAMP approaches
rely on domain-specific symbolic operators to guide the task-level search,
making planning efficient. In this work, we formalize and study the problem of
operator learning for TAMP. Central to this study is the view that operators
define a lossy abstraction of the transition model of a domain. We then propose
a bottom-up relational learning method for operator learning and show how the
learned operators can be used for planning in a TAMP system. Experimentally, we
provide results in three domains, including long-horizon robotic planning
tasks. We find our approach to substantially outperform several baselines,
including three graph neural network-based model-free approaches from the
recent literature. Video: https://youtu.be/iVfpX9BpBRo Code:
https://git.io/JCT0gComment: IROS 202
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
Learning Generalized Reactive Policies using Deep Neural Networks
We present a new approach to learning for planning, where knowledge acquired
while solving a given set of planning problems is used to plan faster in
related, but new problem instances. We show that a deep neural network can be
used to learn and represent a \emph{generalized reactive policy} (GRP) that
maps a problem instance and a state to an action, and that the learned GRPs
efficiently solve large classes of challenging problem instances. In contrast
to prior efforts in this direction, our approach significantly reduces the
dependence of learning on handcrafted domain knowledge or feature selection.
Instead, the GRP is trained from scratch using a set of successful execution
traces. We show that our approach can also be used to automatically learn a
heuristic function that can be used in directed search algorithms. We evaluate
our approach using an extensive suite of experiments on two challenging
planning problem domains and show that our approach facilitates learning
complex decision making policies and powerful heuristic functions with minimal
human input. Videos of our results are available at goo.gl/Hpy4e3
Recommended from our members
The effect of multiple knowledge sources on learning and teaching
Current paradigms for machine-based learning and teaching tend to perform their task in isolation from a rich context of existing knowledge. In contrast, the research project presented here takes the view that bringing multiple sources of knowledge to bear is of central importance to learning in complex domains. As a consequence teaching must both take advantage of and beware of interactions between new and existing knowledge. The central process which connects learning to its context is reasoning by analogy, a primary concern of this research. In teaching, the connection is provided by the explicit use of a learning model to reason about the choice of teaching actions. In this learning paradigm, new concepts are incrementally refined and integrated into a body of expertise, rather than being evaluated against a static notion of correctness. The domain chosen for this experimentation is that of learning to solve "algebra story problems." A model of acquiring problem solving skills in this domain is described, including: representational structures for background knowledge, a problem solving architecture, learning mechanisms, and the role of analogies in applying existing problem solving abilities to novel problems. Examples of learning are given for representative instances of algebra story problems. After relating our views to the psychological literature, we outline the design of a teaching system. Finally, we insist on the interdependence of learning and teaching and on the synergistic effects of conducting both research efforts in parallel
09341 Abstracts Collection -- Cognition, Control and Learning for Robot Manipulation in Human Environments
From 16.08. to 21.08.2009, the Dagstuhl Seminar 09341 ``Cognition, Control and Learning for Robot Manipulation in Human Environments \u27\u27 was held
in Schloss Dagstuhl~--~Leibniz Center for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
- …