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1. Unsupervised Learning of Visual 3D Keypoints for Control

   Chen, Boyuan ; Abbeel, Pieter ; Pathak, Deepak ( July 2021 , International Conference on Machine Learning)

   null (Ed.)

   Learning sensorimotor control policies from highdimensional images crucially relies on the quality of the underlying visual representations. Prior works show that structured latent space such as visual keypoints often outperforms unstructured representations for robotic control. However, most of these representations, whether structured or unstructured are learned in a 2D space even though the control tasks are usually performed in a 3D environment. In this work, we propose a framework to learn such a 3D geometric structure directly from images in an end-toend unsupervised manner. The input images are embedded into latent 3D keypoints via a differentiable encoder which is trained to optimize both a multi-view consistency loss and downstream task objective. These discovered 3D keypoints tend to meaningfully capture robot joints as well as object movements in a consistent manner across both time and 3D space. The proposed approach outperforms prior state-of-art methods across a variety of reinforcement learning benchmarks. Code and videos at https://buoyancy99.github. io/unsup-3d-keypoints/. 

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2. Visually-Grounded Library of Behaviors for Manipulating Diverse Objects across Diverse Configurations and Views

   Yang, Jingyun ; Tung, Hsiao-Yu ; Zhang, Yunchu ; Pathak, Gaurav ; Pokle, Ashwini ; Atkeson, Christopher G ; Fragkiadaki, Katerina ( June 2021 , 5th Annual Conference on Robot Learning)

   We propose a visually-grounded library of behaviors approach for learning to manipulate diverse objects across varying initial and goal configurations and camera placements. Our key innovation is to disentangle the standard image-to-action mapping into two separate modules that use different types of perceptual input:(1) a behavior selector which conditions on intrinsic and semantically-rich object appearance features to select the behaviors that can successfully perform the desired tasks on the object in hand, and (2) a library of behaviors each of which conditions on extrinsic and abstract object properties, such as object location and pose, to predict actions to execute over time. The selector uses a semantically-rich 3D object feature representation extracted from images in a differential end-to-end manner. This representation is trained to be view-invariant and affordance-aware using self-supervision, by predicting varying views and successful object manipulations. We test our framework on pushing and grasping diverse objects in simulation as well as transporting rigid, granular, and liquid food ingredients in a real robot setup. Our model outperforms image-to-action mappings that do not factorize static and dynamic object properties. We further ablate the contribution of the selector's input and show the benefits of the proposed view-predictive, affordance-aware 3D visual object representations. 

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3. Concept Learners for Few-Shot Learning

   Cao, Kaidi ; Brbic, Maria ; Leskovec, Jure ( January 2021 , International Conference on Learning Representations (ICLR))

   null (Ed.)

   Developing algorithms that are able to generalize to a novel task given only a few labeled examples represents a fundamental challenge in closing the gap between machine- and human-level performance. The core of human cognition lies in the structured, reusable concepts that help us to rapidly adapt to new tasks and provide reasoning behind our decisions. However, existing meta-learning methods learn complex representations across prior labeled tasks without imposing any structure on the learned representations. Here we propose COMET, a meta-learning method that improves generalization ability by learning to learn along human-interpretable concept dimensions. Instead of learning a joint unstructured metric space, COMET learns mappings of high-level concepts into semi-structured metric spaces, and effectively combines the outputs of independent concept learners. We evaluate our model on few-shot tasks from diverse domains, including fine-grained image classification, document categorization and cell type annotation on a novel dataset from a biological domain developed in our work. COMET significantly outperforms strong meta-learning baselines, achieving 6–15% relative improvement on the most challenging 1-shot learning tasks, while unlike existing methods providing interpretations behind the model’s predictions. 

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4. Concept Learners for Few-Shot Learning

   Cao, Kaidi ; Brbic, Maria ; Leskovec, Jure. ( January 2021 , International Conference on Learning Representation (ICLR))

   null (Ed.)

   Developing algorithms that are able to generalize to a novel task given only a few labeled examples represents a fundamental challenge in closing the gap between machine- and human-level performance. The core of human cognition lies in the structured, reusable concepts that help us to rapidly adapt to new tasks and provide reasoning behind our decisions. However, existing meta-learning methods learn complex representations across prior labeled tasks without imposing any structure on the learned representations. Here we propose COMET, a meta-learning method that improves generalization ability by learning to learn along human-interpretable concept dimensions. Instead of learning a joint unstructured metric space, COMET learns mappings of high-level concepts into semi-structured metric spaces, and effectively combines the outputs of independent concept learners. We evaluate our model on few-shot tasks from diverse domains, including fine-grained image classification, document categorization and cell type annotation on a novel dataset from a biological domain developed in our work. COMET significantly outperforms strong meta-learning baselines, achieving 6–15% relative improvement on the most challenging 1-shot learning tasks, while unlike existing methods providing interpretations behind the model’s predictions. 

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5. Universal Planning Networks

   Srinivas, A. ; Jabri, A. ; Abbeel, P. ; Levine, S. ; Finn, C. ( January 2018 , International Conference on Machine Learning (ICML))

   A key challenge in complex visuomotor control is learning abstract representations that are ef- fective for specifying goals, planning, and gen- eralization. To this end, we introduce universal planning networks (UPN). UPNs embed differen- tiable planning within a goal-directed policy. This planning computation unrolls a forward model in a latent space and infers an optimal action plan through gradient descent trajectory optimization. The plan-by-gradient-descent process and its un- derlying representations are learned end-to-end to directly optimize a supervised imitation learning objective. We find that the representations learned are not only effective for goal-directed visual imi- tation via gradient-based trajectory optimization, but can also provide a metric for specifying goals using images. The learned representations can be leveraged to specify distance-based rewards to reach new target states for model-free reinforce- ment learning, resulting in substantially more ef- fective learning when solving new tasks described via image-based goals. We were able to achieve successful transfer of visuomotor planning strate- gies across robots with significantly different mor- phologies and actuation capabilities. 

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