Daniela Rus

Professor of Electrical Engineering and Computer Science, MIT

rus@csail.mit.edu

www.csail.mit.edu/person/daniela-rus

Daniela Rus is the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science and Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT.

Her research interests are in robotics, mobile computing, and data science. And is a Class of 2002 MacArthur Fellow, a fellow of ACM, AAAI and IEEE, and a member of the National Academy of Engineering, and the American Academy for Arts and Science. She earned her PhD in Computer Science from Cornell University.

Modular Stiffness Design and Its Simplification on Cable-Driven Continuum Robot Control

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MeMo: Memory as a Model

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BarrierSteer: LLM Safety via Learning Barrier Steering

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MineDraft: A Framework for Batch Parallel Speculative Decoding

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3-RRS and 3-RRUR parallel manipulators as spatial positioning manipulators

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Two-finger soft gripper with a scaled-pad contact interface for dual-mode grasping and surface cleaning

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Shape Control of a Planar Hyper-Redundant Robot via Hybrid Kinematics-Informed and Learning-based Approach

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Perception-Aware Multimodal Spatial Reasoning from Monocular Images

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GLASS: A Global and Local Adaptive Segmentation System for Robust Transparent Object Perception

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ZipFold: Modular Actuators for Scaleable Adaptive Robots

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Physical Human-Robot Interaction for Grasping in Augmented Reality via Rigid-Soft Robot Synergy

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Adaptive and Multi-object Grasping via Deformable Origami Modules

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ActiveDPO: Active Direct Preference Optimization for Sample-Efficient Alignment

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A general soft robotic controller inspired by neuronal structural and plastic synapses that adapts to diverse arms, tasks, and perturbations

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MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents

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ReasonIR: Training Retrievers for Reasoning Tasks

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TETRIS: Optimal Draft Token Selection for Batch Speculative Decoding

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Active Human Feedback Collection via Neural Contextual Dueling Bandits

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DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning

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