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VoxAct-B: Voxel-Based Acting and Stabilizing Policy for Bimanual Manipulation
By I-Chun Arthur Liu Bimanual manipulation is essential for robots to manipulate objects as well as humans. It becomes necessary when objects are too large to be controlled by one hand or when using one hand to stabilize an object makes it easier for the other hand to manipulate it. In this work, we focus…
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ViSaRL: Saliency-Guided Visual Representations for Robot Learning
By Anthony Liang Teaching robots to perform complex control tasks from high-dimensional image inputs is a nontrivial problem. State-of-the-art methods require hundreds of manually collected expert demonstrations or hours to days of training in simulation to learn a simple pick-and-place skill. One reason for this is that images are cluttered with distractions for robots, such…
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Language Models meet Classical Planners to make smarter Robot Task Plans
By Ishika Singh Everyday household tasks require both common sense understanding of the world and situated knowledge about the current environment. To create a task plan for “Make dinner” an agent needs common sense: object affordances, such as the stove can be used to make food; logical sequences of actions, such as the oven must be preheated…
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pyribs: Accelerating Quality Diversity Research
Quality Diversity algorithms identify many diverse solutions to solve problems rather than a single best option. Researchers developed pyribs to facilitate development of research projects that use Quality Diversity.
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LLMs can help robots learn new tasks in unfamiliar places
Large language models can help our robots adapt to new tasks and situations by enabling better pre-training and by guiding them in unfamiliar settings.