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Explore hierarchical planning and reinforcement learning for robotics, from humanoid control fundamentals to advanced applications with large models and policy co-training techniques.
Explore the design and tooling of autonomous learning agents, connecting autonomous systems with agentic models to achieve effective real-world reinforcement learning.
Explore concepts of learning representations for plan generalization, connecting system identification, sim-to-real, meta reinforcement learning, and in-context learning with their limitations.
Explore how autonomous learning agents require careful design to achieve effective interaction, connecting autonomous systems with agentic models for real-world reinforcement learning.
Explore visual goal-condition reinforcement learning for robotics, focusing on representation learning techniques that translate image-based goals into functional latent spaces for effective agent instruction.
Explore goal conditioning and hierarchical planning in robot learning, including optimal training methods, goal distributions, and generalization strategies for better model reuse in complex tasks.
Explore the latest advancements in Gemini Robotics through Keerthana Gopalakrishnan's presentation on recent developments and findings in robot learning technology.
Explore groundbreaking advancements in 3D vision through CroCo, DUSt3R, and MASt3R frameworks, revolutionizing multi-view stereo reconstruction and geometric deep learning for enhanced robotic navigation and cultural preservation.
Build a Generalist Robotics Policy from scratch, reimplementing the "Octo" model step-by-step. Learn to create, train, and evaluate large models for robotics using interaction data and transformer architecture.
Explore innovative approaches to integrating foundation models in robotics, focusing on multi-robot collaboration and articulated object reconstruction for enhanced real-world applications.
Discover how robots can build actionable representations of dynamic environments, detect semantic changes, and improve their perception capabilities through real-time autonomous operation and scene prediction.
Explore how large-scale simulation training produces proficient robotic agents capable of real-world navigation and manipulation using only RGB sensors, guided by language instructions.
Explore state-of-the-art View Transformers for 3D object manipulation in robotics, including RVT and RVT-2, offering improved task success, faster training, and real-world applicability.
Explore cutting-edge robot learning techniques and advancements in AI-driven robotics through expert presentations and discussions at the Mila seminar.
Explore Ubisoft La Forge's ML-based game actors, challenges in AAA games, and innovative approaches to simulating player behavior through concrete in-game examples.
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