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

Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling

Article discussing models for increasing the reliability of computational methods for identifying functionally important sites from biomolecular sequences

Notes on the taxonomy, geography, and ecology of the piliferous campylopus species in the Netherlands and N.W. Germany

<p><b>Copyright information:</b></p><p>Taken from "Glycosylation site prediction using ensembles of Support Vector Machine classifiers"</p><p>http://www.biomedcentral.com/1471-2105/8/438</p><p>BMC Bioinformatics 2007;8():438-438.</p><p>Published online 9 Nov 2007</p><p>PMCID:PMC2220009.</p><p></p> of the data extracted from the original glycoprotein sequence dataset for C-linked glycosylation using local sequence identity with 0/1 String Kernel