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1. Learning to Compose Hierarchical Object-Centric Controllers for Robotic Manipulation

   Sharma, M.; Liang, J.; Zhao, J.; LaGrassa, A.; Kroemer, O. (October 2020, Conference on Robot Learning)

   null (Ed.)

   Manipulation tasks can often be decomposed into multiple subtasks performed in parallel, e.g., sliding an object to a goal pose while maintaining con- tact with a table. Individual subtasks can be achieved by task-axis controllers defined relative to the objects being manipulated, and a set of object-centric controllers can be combined in an hierarchy. In prior works, such combinations are defined manually or learned from demonstrations. By contrast, we propose using reinforcement learning to dynamically compose hierarchical object-centric controllers for manipulation tasks. Experiments in both simulation and real world show how the proposed approach leads to improved sample efficiency, zero-shot generalization to novel test environments, and simulation-to-reality transfer with- out fine-tuning. 

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2. Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics

   Chen, Yenho; Mudrik, Noga; Johnsen, Kyle A; Alagapan, Sankaraleengam; Charles, Adam S; Rozell, Christopher J (September 2024, Open Review)

   Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according to simple locally linear dynamics. However, existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity due to noise-sensitive inference procedures and limited model formulations. This can lead to inconsistent results on signals with similar dynamics, limiting the model's ability to provide scientific insight. In this work, we address these limitations and propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise. Additionally, we introduce an extended latent dynamics model to improve robustness against system nonlinearities. We evaluate our approach on several synthetic dynamical systems, including an empirically-derived brain-computer interface experiment, and demonstrate more accurate latent variable inference in nonlinear systems with diverse noise conditions. Furthermore, we apply our method to a real-world clinical neurophysiology dataset, illustrating the ability to identify interpretable and coherent structure where previous models cannot. 

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3. OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation

   Ren, Hongyu; Zhu, Yuke; Leskovec, Jure; Anandkumar, Anima; Garg, Animesh. (January 2020, Conference on Uncertainty in Artificial Intelligence (UAI))

   Real-world tasks often exhibit a compositional structure that contains a sequence of simpler sub-tasks. For instance, opening a door requires reaching, grasping, rotating, and pulling the door knob. Such compositional tasks require an agent to reason about the sub-task at hand while orchestrating global behavior accordingly. This can be cast as an online task inference problem, where the current task identity, represented by a context variable, is estimated from the agent’s past experiences with probabilistic inference. Previous approaches have employed simple latent distributions, e.g., Gaussian, to model a single context for the entire task. However, this formulation lacks the expressiveness to capture the composition and transition of the sub-tasks. We propose a variational inference framework OCEAN to perform online task inference for compositional tasks. OCEAN models global and local context variables in a joint latent space, where the global variables represent a mixture of subtasks required for the task, while the local variables capture the transitions between the subtasks. Our framework supports flexible latent distributions based on prior knowledge of the task structure and can be trained in an unsupervised manner. Experimental results show that OCEAN provides more effective task inference with sequential context adaptation and thus leads to a performance boost on complex, multi-stage tasks. 

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4. Scalable Gaussian Process Variational Autoencoders

   Jazbec, Metod; Ashman, Matthew; Fortuin, Vincent; Pearce, Michael; Mandt, Stephan; Raetsch, Gunnar (April 2021, Proceedings of Machine Learning Research)

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   Conventional variational autoencoders fail in modeling correlations between data points due to their use of factorized priors. Amortized Gaussian process inference through GPVAEs has led to significant improvements in this regard, but is still inhibited by the intrinsic complexity of exact GP inference. We improve the scalability of these methods through principled sparse inference approaches. We propose a new scalable GPVAE model that outperforms existing approaches in terms of runtime and memory footprint, is easy to implement, and allows for joint end-to-end optimization of all components 

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5. Scalable Gaussian Process Variational Autoencoders

   Jazbec, M.; Ashman, M.; Fortuin, V.; Pearce, M.; Mandt, S.; Rätsch, G. (September 2021, nternational Conference on Artificial Intelligence and Statistics)

   null (Ed.)

   Conventional variational autoencoders fail in modeling correlations between data points due to their use of factorized priors. Amortized Gaussian process inference through GPVAEs has led to significant improvements in this regard, but is still inhibited by the intrinsic complexity of exact GP inference. We improve the scalability of these methods through principled sparse inference approaches. We propose a new scalable GPVAE model that outperforms existing approaches in terms of runtime and memory footprint, is easy to implement, and allows for joint end-to-end optimization of all components. 

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