Pierre Sermanet
4 papers in the PaperMetrix corpus
Papers by this author
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Broadly-Exploring, Local-Policy Trees for Long-Horizon Task Planning
2020 · arXiv (Cornell University)
Long-horizon planning in realistic environments requires the ability to reason over sequential tasks in high-dimensional state spaces with complex dynamics. Classical motion planning algorithms, such as rapidly-exploring random trees, are capable of efficiently exploring large …
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GoalsEye: Learning High Speed Precision Table Tennis on a Physical Robot
2022 · arXiv (Cornell University)
Learning goal conditioned control in the real world is a challenging open problem in robotics. Reinforcement learning systems have the potential to learn autonomously via trial-and-error, but in practice the costs of manual reward design, …
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Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
2022 · arXiv (Cornell University)
Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extended instructions expressed in natural language. However, a …
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Inner Monologue: Embodied Reasoning through Planning with Language Models
2022 · arXiv (Cornell University)
Recent works have shown how the reasoning capabilities of Large Language Models (LLMs) can be applied to domains beyond natural language processing, such as planning and interaction for robots. These embodied problems require an agent …