About

Daniela Rus is a professor affiliated with the Department of Electrical Engineering and Computer Science (EECS) at MIT. Her research focuses on artificial intelligence and decision-making, combining intellectual traditions from computer science and electrical engineering to develop techniques for the analysis and synthesis of systems that interact with the external world through perception, communication, and action. Her work involves systems that learn, make decisions, and adapt to changing environments, contributing to advancements in AI for healthcare, life sciences, and societal applications. As a leading figure in her field, Daniela Rus's research encompasses a broad range of topics including robotics, machine learning, and intelligent systems. Her contributions aim to develop groundbreaking sensors, energy transducers, and physical substrates for computation, addressing shared challenges facing humanity through innovative system design and AI integration.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Machine Learning
• Control engineering
• Computer vision
• Human–computer interaction
• Mechanical engineering
• Mathematical analysis
• Mathematics
• Nanotechnology
• Data science
• Materials science
• Embedded system
• Aerospace engineering
• Applied mathematics
• Physics
• Real-time computing
• Systems engineering

Selected publications

• Vacuum-Driven Origami Muscle for Wearable Knee Assistance

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• ZipFold: Modular Actuators for Scaleable Adaptive Robots

  arXiv (Cornell University) · 2026-04-06

  preprintOpen accessSenior author

  There is a growing need for robots that can change their shape, size and mechanical properties to adapt to evolving tasks and environments. However, current shape-changing systems generally utilize bespoke, system-specific mechanisms that can be difficult to scale, reconfigure or translate from one application to another. This paper introduces a compact, easy-to-fabricate deployable actuator that achieves reversible scale and stiffness transformations through compound folding and zipping of flexible 3D-printed plastic strips into square-section deployable beams. The simple actuation method allows for smooth, continuous transitions between compact (flexible) and expanded (quasi-rigid) states, facilitating diverse shape and stiffness transformations when modules are combined into larger assemblies. The actuator's mechanical performance is characterized and an integrated system involving a four-module adaptive walking robot is demonstrated.

  PublisherDOI

• Adaptive and Multi-object Grasping via Deformable Origami Modules

  2026-04-07

  articleOpen access

  Soft robotics grippers have shown great promise in handling fragile and geometrically complex objects, as a less complex alternative to sensor-heavy conventional gripper systems. In this work, we present a multi-finger hybrid gripper featuring passively deformable origami modules that generate constant force and torque output. All fingers are driven by a 1-DOF actuator mechanism, and equipped with parallel origami modules enabling passive shape adaptability and stable grasping force without active sensing or feedback control. More importantly, we demonstrate an interesting capability in simultaneous multi-object grasping, which allows stacked objects of varied shapes and sizes to be picked, transported and placed independently, significantly improving manipulation efficiency compared to single-object grasping. These results highlight the potential of origami-based compliant structures as scalable modules for adaptive, stable and efficient multiobject manipulation in domestic and industrial pick-and-place scenarios.

  PublisherOA PDFDOI

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

  Science Advances · 2026-01-07 · 1 citations

  articleOpen access

  Human intelligence arises from the interplay between a compliant morphology and a cognitive system that is capable of adaptive learning. Soft robots exhibit similar mechanical compliance, but they still need learning capabilities that can be generalized across tasks and adapted to unknown conditions. We present a neuron-inspired control framework that couples a paired offline-online decomposition with a learned contraction metric. Offline "structural synapses" encode task-agnostic features, while online "plastic synapses" are configuration-specific parameters updated by error-gated rules consistent with long-term potentiation and depression. The contraction metric serves as a homeostatic constraint, providing a stability guarantee. We validate our approach on cable-driven and shape-memory-alloy soft arms across trajectory tracking, pick-and-place, and whole-body shaping tasks. Compared with baseline methods, our approach reduces tracking error by 44 to 55% and maintains more than 92% shape accuracy under perturbations, including varying payloads, dynamic airflow, and actuator failures. These results establish a general controller that adapts to diverse soft arms, tasks, and perturbations.

  PublisherDOI

• Flex: End-to-End Text-Instructed Visual Navigation From Foundation Model Features

  IEEE Robotics and Automation Letters · 2026-03-27

  articleSenior author

  End-to-end learning directly maps sensory inputs to actions, creating highly integrated and efficient policies for complex robotics tasks. However, such models often struggle to generalize beyond their training scenarios, limiting adaptability to new environments, tasks, and concepts. In this work, we investigate the minimal data requirements and architectural adaptations necessary to achieve robust closed-loop performance with vision-based control policies under unseen text instructions and visual distribution shifts. Our findings are synthesized in Flex (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i>ly-<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lex</i>ically), a framework that uses pre-trained Vision Language Models (VLMs) as frozen patch-wise feature extractors, generating spatially aware embeddings that integrate semantic and visual information. We demonstrate the effectiveness of this approach on a quadrotor fly-to-target task, where agents trained via behavior cloning on a small simulated dataset (with zero real-world images) successfully generalize to real-world scenes with diverse novel goals and command formulations.

  PublisherDOI

• RealTwin: Concept Graph Representation and Grounding Framework for Reality-Preserving Digital Twin Reconstruction

  2026-04-13 · 1 citations

  articleOpen access

  Reconstructing realistic digital twins has become crucial as advances in mixed reality, metaverse, and robotics demand more accurate simulations for the physical world. Despite technical progress, building high-fidelity digital twins from a systematic and human-centered perspective remains underexplored. Drawing from the human processing model, we decompose human-centric reality into perception, motion, and cognition, and define a reality-preserving digital twin (RPDT) as a reconstruction integrating these dimensions. We present RealTwin, an attribute-graph-based representation and inference framework for RPDT. Leveraging the grounding capabilities of Multimodal Large Language Models (MLLMs), RealTwin chains AI tools to construct attribute graphs that faithfully encode real-world properties. We validate RealTwin through both technical evaluation, showing promising success in graph parsing and attribute inference, and a user study, assessing its applicability across diverse user groups. Enlightened by RealTwin, we discuss critical issues, including ecology, interaction space, and real-world adoption, for future end-to-end, fine-grained, and scalable digital twin reconstruction.

  PublisherDOI

• Cooperation by non-kin during birth underpins sperm whale social complexity

  Science · 2026-03-26

  articleCorresponding

  We quantitatively document a sperm whale birth event, revealing collective support behaviors across kinship lines. Using high-resolution drone footage, computer vision, and multiscale network analysis, we studied the interactions within a Caribbean sperm whale unit comprising two matrilines. Our results suggest that a female family member led birth assistance and that after delivery, all individuals oriented toward and helped lift the newborn, taking turns in a coordinated, cross-kin effort. Despite historically observed foraging segregation, kinship barriers dissolved as all unit members contributed. These analyses provide evidence of birth attendance, or assistance, in a nonprimate species, a behavior long considered characteristic only of humans and their close relatives.

  PublisherDOI

• Faster algorithms for growing collision-free convex polytopes in robot configuration space

  The International Journal of Robotics Research · 2026-05-22

  preprintOpen accessSenior author

  We propose two novel algorithms for constructing convex probabilistically collision-free polytopes in robot configuration space. Finding these polytopes enables the application of stronger motion-planning frameworks such as trajectory optimization with Graphs of Convex Sets ( Marcucci et al., 2023 ) and is currently a major roadblock in the adoption of these approaches. In this paper, we build upon the IRIS-NP algorithm (Iterative Regional Inflation by Semidefinite &amp; Nonlinear Programming) of Petersen and Tedrake (2023) to significantly improve tunability, runtimes, and scaling to complex environments. IRIS-NP uses nonlinear programming paired with uniform random initialization to find configurations on the boundary of the free configuration space. Our key insight is that finding nearby configuration-space obstacles using sampling is inexpensive and greatly accelerates region generation. We propose two algorithms using such samples to either employ nonlinear programming more efficiently (IRIS-NP2) or circumvent it altogether using a massively parallel zero-order optimization strategy (IRIS-ZO). Both algorithms employ a novel termination condition that controls the probability of exceeding a user-specified permissible fraction-in-collision, eliminating a significant source of tuning difficulty in IRIS-NP. We further present an approach for applying both algorithms in parametrized configuration spaces. We compare the performance across eight robot environments, showing that IRIS-ZO achieves an order-of-magnitude speed advantage over IRIS-NP, which is extended roughly by an additional order of magnitude by parallelizing it with a GPU. IRIS-NP2, also significantly faster than IRIS-NP, builds larger polytopes using fewer hyperplanes which has the additional benefit of accelerating downstream motion planning. Website: https://sites.google.com/view/fastiris .

  PublisherOA PDFDOI

• “Data will solve robotics and automation: True or false?”: A debate

  Science Robotics · 2025-08-27

  review

  Leading researchers debate the long-term influence of model-free methods that use large sets of demonstration data to train numerical generative models to control robots.

  PublisherDOI

• Safe Motion Planning and Control Using Predictive and Adaptive Barrier Methods for Autonomous Surface Vessels

  ArXiv.org · 2025-10-01

  preprintOpen accessSenior author

  Safe motion planning is essential for autonomous vessel operations, especially in challenging spaces such as narrow inland waterways. However, conventional motion planning approaches are often computationally intensive or overly conservative. This paper proposes a safe motion planning strategy combining Model Predictive Control (MPC) and Control Barrier Functions (CBFs). We introduce a time-varying inflated ellipse obstacle representation, where the inflation radius is adjusted depending on the relative position and attitude between the vessel and the obstacle. The proposed adaptive inflation reduces the conservativeness of the controller compared to traditional fixed-ellipsoid obstacle formulations. The MPC solution provides an approximate motion plan, and high-order CBFs ensure the vessel's safety using the varying inflation radius. Simulation and real-world experiments demonstrate that the proposed strategy enables the fully-actuated autonomous robot vessel to navigate through narrow spaces in real time and resolve potential deadlocks, all while ensuring safety.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: An Expedition in Computing for Compiling Printable Programmable Machines

  NSF · $6.6M · 2012–2019

• NSFSaTC-BSF: TWC: Small: Enabling Secure and Private Cloud Computing using Coresets

  NSF · $486k · 2015–2020

• EFRI C3 SoRo: Soft, Strong, and Safe Configurable Robots for Diverse Manipulation Tasks

  NSF · $2.0M · 2018–2024

• ITR: Collaborative Research: -\(NHS+ASE)-\(int+dmc\): Networks of Robots and Sensors for First Responders

  NSF · $673k · 2004–2010

• S&AS: INT: COLLAB: Autonomy as a Service

  NSF · $235k · 2017–2021

Frequent coauthors

• Sertaç Karaman

  Massachusetts Institute of Technology

  101 shared

• Mac Schwager

  Vaughn College of Aeronautics and Technology

  75 shared

• Ramin Hasani

  66 shared

• Alexander Amini

  64 shared

• Emilio Frazzoli

  61 shared

• Marcelo H. Ang

  56 shared

• Guy Rosman

  55 shared

• Mathias Lechner

  51 shared

Awards & honors

• 2025-26 EECS Faculty Award Roundup