54 research outputs found
Behavior-grounded multi-sensory object perception and exploration by a humanoid robot
Infants use exploratory behaviors to learn about the objects around them. Psychologists have theorized that behaviors such as touching, pressing, lifting, and dropping enable infants to form grounded object representations. For example, scratching an object can provide information about its roughness, while lifting it can provide information about its weight. In a sense, the exploratory behavior acts as a ``question\u27\u27 to the object, which is subsequently ``answered by the sensory stimuli produced during the execution of the behavior. In contrast, most object representations used by robots today rely solely on computer vision or laser scan data, gathered through passive observation. Such disembodied approaches to robotic perception may be useful for recognizing an object using a 3D model database, but nevertheless, will fail to infer object properties that cannot be detected using vision alone. To bridge this gap, this dissertation introduces a framework for object perception and exploration in which the robot\u27s representation of objects is grounded in its own sensorimotor experience with them. In this framework, an object is represented by sensorimotor contingencies that span a diverse set of exploratory behaviors and sensory modalities. The results from several large-scale experimental studies show that the behavior-grounded object representation enables a robot to solve a wide variety of tasks including recognition of objects based on the stimuli that they produce, object grouping and sorting, and learning category labels that describe objects and their properties
A Framework for Few-Shot Policy Transfer through Observation Mapping and Behavior Cloning
Despite recent progress in Reinforcement Learning for robotics applications,
many tasks remain prohibitively difficult to solve because of the expensive
interaction cost. Transfer learning helps reduce the training time in the
target domain by transferring knowledge learned in a source domain. Sim2Real
transfer helps transfer knowledge from a simulated robotic domain to a physical
target domain. Knowledge transfer reduces the time required to train a task in
the physical world, where the cost of interactions is high. However, most
existing approaches assume exact correspondence in the task structure and the
physical properties of the two domains. This work proposes a framework for
Few-Shot Policy Transfer between two domains through Observation Mapping and
Behavior Cloning. We use Generative Adversarial Networks (GANs) along with a
cycle-consistency loss to map the observations between the source and target
domains and later use this learned mapping to clone the successful source task
behavior policy to the target domain. We observe successful behavior policy
transfer with limited target task interactions and in cases where the source
and target task are semantically dissimilar.Comment: Paper accepted to the IROS 2023 Conferenc
Automaton-Guided Curriculum Generation for Reinforcement Learning Agents
Despite advances in Reinforcement Learning, many sequential decision making
tasks remain prohibitively expensive and impractical to learn. Recently,
approaches that automatically generate reward functions from logical task
specifications have been proposed to mitigate this issue; however, they scale
poorly on long-horizon tasks (i.e., tasks where the agent needs to perform a
series of correct actions to reach the goal state, considering future
transitions while choosing an action). Employing a curriculum (a sequence of
increasingly complex tasks) further improves the learning speed of the agent by
sequencing intermediate tasks suited to the learning capacity of the agent.
However, generating curricula from the logical specification still remains an
unsolved problem. To this end, we propose AGCL, Automaton-guided Curriculum
Learning, a novel method for automatically generating curricula for the target
task in the form of Directed Acyclic Graphs (DAGs). AGCL encodes the
specification in the form of a deterministic finite automaton (DFA), and then
uses the DFA along with the Object-Oriented MDP (OOMDP) representation to
generate a curriculum as a DAG, where the vertices correspond to tasks, and
edges correspond to the direction of knowledge transfer. Experiments in
gridworld and physics-based simulated robotics domains show that the curricula
produced by AGCL achieve improved time-to-threshold performance on a complex
sequential decision-making problem relative to state-of-the-art curriculum
learning (e.g, teacher-student, self-play) and automaton-guided reinforcement
learning baselines (e.g, Q-Learning for Reward Machines). Further, we
demonstrate that AGCL performs well even in the presence of noise in the task's
OOMDP description, and also when distractor objects are present that are not
modeled in the logical specification of the tasks' objectives.Comment: To be presented at The International Conference on Automated Planning
and Scheduling (ICAPS) 202
Using ConceptGrid as an easy authoring technique to check natural language responses
ConceptGrid provides a template-style approach to check natural language responses by students using a model-tracing style intelligent tutoring system. The tutor-author creates, using a web-based authoring system, a latticestyle structure that contains the set of required concepts that need to be in a student response. The author can also create just-in-time feedback based on the concepts present or absent in the student\u27s response. ConceptGrid is integrated within the xPST authoring tool and was tested in two experiments, both of which show the efficacy of the technique to check student answers. The first study tested the tutor\u27s effectiveness overall in the domain of statistics. The second study investigated ConceptGrid\u27s use by non-programmers and non-cognitive scientists. ConceptGrid extends existing capabilities for authoring of intelligent tutors by using this template-based approach for checking sentence-length natural language input
Autonomous System-Level Fault Diagnosis in Satellites Using Housekeeping Telemetry
To continue the growing success of scientific discovery through deep space exploration, missions need to be able to be able to travel further and operate more efficiently than ever before. To ensure resilience in this capability, on-board autonomous fault mitigation methods must be developed and matured. To this end, we present a system for cross-subsystem fault diagnosis of satellites using spacecraft telemetry. Our system leverages a combination of Kalman Filters, Autoencoders, and Causality algorithms. We test our system for accuracy against three data sets of varying complexity levels, along with baseline testing. Additionally, we perform an ablation study to evaluate on-board tractability
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Notes on the taxonomy, geography, and ecology of the piliferous campylopus species in the Netherlands and N.W. Germany
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