Marco Hutter

• ETH Zurich

Robot autonomy in unknown, GPS-denied, and complex underground environments requires real-time, robust, and accurate onboard pose estimation and mapping for reliable operations. This becomes particularly challenging in perception-degraded subterranean conditions under harsh environmental factors, including darkness, dust, and geometrically self-sim...

Preference-based Reinforcement Learning (PbRL) enables policy learning through simple queries comparing trajectories from a single policy. While human responses to these queries make it possible to learn policies aligned with human preferences, PbRL suffers from low query efficiency, as policy bias limits trajectory diversity and reduces the number...

Ensuring safe navigation in complex environments requires accurate real-time traversability assessment and understanding of environmental interactions relative to the robot`s capabilities. Traditional methods, which assume simplified dynamics, often require designing and tuning cost functions to safely guide paths or actions toward the goal. This p...

Achieving robust autonomy in mobile robots operating in complex and unstructured environments requires a multimodal sensor suite capable of capturing diverse and complementary information. However, designing such a sensor suite involves multiple critical design decisions, such as sensor selection, component placement, thermal and power limitations,...

Climbing robots hold significant promise for applications such as industrial inspection and maintenance, particularly in hazardous or hard-to-reach environments. This paper describes the quadrupedal climbing robot Magnecko, developed with the major goal of providing a research platform for legged climbing locomotion. With its 12 actuated degrees of...

Mobile robots on construction sites require accurate pose estimation to perform autonomous surveying and inspection missions. Localization in construction sites is a particularly challenging problem due to the presence of repetitive features such as flat plastered walls and perceptual aliasing due to apartments with similar layouts inter and intra...

Seamless operation of mobile robots in challenging environments requires low-latency local motion estimation (e.g., dynamic maneuvers) and accurate global localization (e.g., wayfinding). While most existing sensor-fusion approaches are designed for specific scenarios, this work introduces a flexible open-source solution for task- and setup-agnosti...

The autonomous transportation of materials over challenging terrain is a challenge with major economic implications and remains unsolved. This paper introduces LEVA, a high-payload, high-mobility robot designed for autonomous logistics across varied terrains, including those typical in agriculture, construction, and search and rescue operations. LE...

Place recognition is essential to maintain global consistency in large-scale localization systems. While research in urban environments has progressed significantly using LiDARs or cameras, applications in natural forest-like environments remain largely under-explored. Furthermore, forests present particular challenges due to high self-similarity a...

Accurate positioning is crucial in the construction industry, where labor shortages highlight the need for automation. Robotic systems with long kinematic chains are required to reach complex workspaces, including floors, walls, and ceilings. These requirements significantly impact positioning accuracy due to effects such as deflection and backlash...

Tendon-driven continuum soft robots are currently applied in research and are given a promising perspective for future applications. For the routing of the tendons from the actuator to the point where the loading is demanded, two routing possibilities exist in the literature: internal routing of the tendons with the help of structurally embedded Bo...

Walking robots have advanced rapidly over the last decade and have reached a maturity level where commercial applications have become viable. However, robotic exploration of celestial bodies has been performed solely with wheeled systems until today, making access to highly unstructured, compressible, and sloped areas very challenging. Thus, it is...

Autonomous robot navigation in off–road environments requires a comprehensive understanding of the terrain geometry and traversability. The degraded perceptual conditions and sparse geometric information at longer ranges make the problem challenging especially when driving at high speeds. Furthermore, the sensing–to–mapping latency and the look–ahe...

This paper presents a novel CMOS magnetic field sensor designed for the simultaneous measurement of all three magnetic field components (Bx, By, and Bz) at a single location. The sensor incorporates three sets of mutually orthogonal horizontal and vertical Hall-effect elements, each equipped with dedicated biasing circuits and amplifiers. With a co...

Accurate, efficient, and robust state estimation is more important than ever in robotics as the variety of platforms and complexity of tasks continue to grow. Historically, discrete-time filters and smoothers have been the dominant approach, in which the estimated variables are states at discrete sample times. The paradigm of continuous-time state...

Identifying the physical properties of the surrounding environment is essential for robotic locomotion and navigation to deal with non-geometric hazards, such as slippery and deformable terrains. It would be of great benefit for robots to anticipate these extreme physical properties before contact; however, estimating environmental physical paramet...

Reinforcement learning (RL) often necessitates a meticulous Markov Decision Process (MDP) design tailored to each task. This work aims to address this challenge by proposing a systematic approach to behavior synthesis and control for multi-contact loco-manipulation tasks, such as navigating spring-loaded doors and manipulating heavy dishwashers. We...

The precise and safe control of heavy material handling machines presents numerous challenges due to the hard-to-model hydraulically actuated joints and the need for collision-free trajectory planning with a free-swinging end-effector tool. In this work, we propose an RL-based controller that commands the cabin joint and the arm simultaneously. It...

Construction sites are challenging environments for autonomous systems due to their unstructured nature and the presence of dynamic actors, such as workers and machinery. This work presents a comprehensive panoptic scene understanding solution designed to handle the complexities of such environments by integrating 2D panoptic segmentation with 3D L...

Autonomous robot navigation in off-road environments requires a comprehensive understanding of the terrain geometry and traversability. The degraded perceptual conditions and sparse geometric information at longer ranges make the problem challenging especially when driving at high speeds. Furthermore, the sensing-to-mapping latency and the look-ahe...

Pedipulation leverages the feet of legged robots for mobile manipulation, eliminating the need for dedicated robotic arms. While previous works have showcased blind and task-specific pedipulation skills, they fail to account for static and dynamic obstacles in the environment. To address this limitation, we introduce a reinforcement learning-based...

Using doors is a longstanding challenge in robotics and is of significant practical interest in giving robots greater access to human-centric spaces. The task is challenging due to the need for online adaptation to varying door properties and precise control in manipulating the door panel and navigating through the confined doorway. To address this...

Identifying the physical properties of the surrounding environment is essential for robotic locomotion and navigation to deal with non-geometric hazards, such as slippery and deformable terrains. It would be of great benefit for robots to anticipate these extreme physical properties before contact; however, estimating environmental physical paramet...

The ICP registration algorithm has been a preferred method for LiDAR-based robot localization for nearly a decade. However, even in modern SLAM solutions, ICP can degrade and become unreliable in geometrically ill-conditioned environments. Current solutions primarily focus on utilizing additional sources of information, such as external odometry, t...

Reconstructing the 3D shape of a deformable environment from the information captured by a moving depth camera is highly relevant to surgery. The underlying challenge is the fact that simultaneously estimating camera motion and tissue deformation in a fully deformable scene is an ill-posed problem, especially from a single arbitrarily moving viewpo...

In unstructured environments the best path is not always the shortest, but needs to consider various objectives like energy efficiency, risk of failure or scientific outcome. This paper proposes a global planner, based on the A* algorithm, capable of individually considering multiple layers of map data for different cost objectives. We introduce we...

Significant infrastructure is required to establish a long-term presence of humans on the lunar surface. In-situ resource utilization (ISRU) is a fundamental approach to ensure the viability of such construction. Here, we investigate the feasibility of constructing blast shields as one example of lunar infrastructure using unprocessed lunar boulder...

Legged locomotion has recently achieved remarkable success with the progress of machine learning techniques, especially deep reinforcement learning (RL). Controllers employing neural networks have demonstrated empirical and qualitative robustness against real-world uncertainties, including sensor noise and external perturbations. However, formally...

Autonomous wheeled-legged robots have the potential to transform logistics systems, improving operational efficiency and adaptability in urban environments. Navigating urban environments, however, poses unique challenges for robots, necessitating innovative solutions for locomotion and navigation. These challenges include the need for adaptive loco...

The exploration of large-scale unknown environments can benefit from the deployment of multiple robots for collaborative mapping. Each robot explores a section of the environment and communicates onboard pose estimates and maps to a central server to build an optimized global multi-robot map. Naturally, inconsistencies can arise between onboard and...

Performing agile navigation with four-legged robots is a challenging task because of the highly dynamic motions, contacts with various parts of the robot, and the limited field of view of the perception sensors. Here, we propose a fully learned approach to training such robots and conquer scenarios that are reminiscent of parkour challenges. The me...

Legged locomotion is a complex control problem that requires both accuracy and robustness to cope with real-world challenges. Legged systems have traditionally been controlled using trajectory optimization with inverse dynamics. Such hierarchical model-based methods are appealing because of intuitive cost function tuning, accurate planning, general...

This article presents the CERBERUS robotic system-of-systems, which won the DARPA Subterranean Challenge Final Event in 2021. The Subterranean Challenge was organized by DARPA with the vision to facilitate the novel technologies necessary to reliably explore diverse underground environments despite the grueling challenges they present for robotic a...

Quadrupedal robots consume large amounts of energy while walking or standing, limiting their range and operation time. Moreover, their design involves multiple trade-offs between conflicting requirements, reducing their flexibility and suitability for certain applications despite their advantages in rough and unknown environments. One of the root c...

In situ robotic construction is a type of construction where mobile robotic systems build directly on the building site. To enable on-site navigation, industrial robots can be integrated with mobile bases, while mobile, high-payload construction machines can be adapted for autonomous operation. With parallel advances in sensor processing, these rob...

Autonomous navigation at high speeds in off-road environments necessitates robots to comprehensively understand their surroundings using onboard sensing only. The extreme conditions posed by the off-road setting can cause degraded image quality as well as limited sparse geometric information available from LiDAR sensing when driving at high speeds....

Accurate positioning is crucial in the construction industry, where labor shortages highlight the need for automation. Robotic systems with long kinematic chains are required to reach complex workspaces, including floors, walls, and ceilings. These requirements significantly impact positioning accuracy due to effects such as deflection and backlash...

Coordinated navigation of an arbitrary number of robots to an arbitrary number of goals is a big challenge in robotics, often hindered by scalability limitations of existing strategies. This paper introduces a decentralized multi-agent control system using neural network policies trained in simulation. By leveraging permutation invariant neural net...

Logistics and service operations involving parcel preparation, delivery, and unpacking from a supply point to a user’s home could be carried out completely by robots in the near future, taking advantage of the capabilities of the different robot morphologies for the logistics, outdoor, and domestic environments. The use of robots for parcel deliver...

This article presents a hybrid motion planning and control approach applicable to various ground robot types and morphologies. Our two-step approach uses a sampling-based planner to compute an approximate motion, which is then fed to numerical optimization for refinement. The sampling-based stage finds a long-term global plan consisting of a contac...

Automated building processes that enable efficient in situ resource utilization can facilitate construction in remote locations while simultaneously offering a carbon-reducing alternative to commonplace building practices. Toward these ends, we present a robotic construction pipeline that is capable of planning and building freeform stone walls and...

Mobile ground robots require perceiving and understanding their surrounding support surface to move around autonomously and safely. The support surface is commonly estimated based on exteroceptive depth measurements, e.g., from LiDARs. However, the measured depth fails to align with the true support surface in the presence of high grass or other pe...

This letter introduces Barry, a dynamically balancing quadruped robot optimized for high payload capabilities and efficiency. It presents a new high-torque and low-inertia leg design, which includes custom-built high-efficiency actuators and transparent, sensorless transmissions. The robot's reinforcement learning-based controller is trained to ful...

Dynamic locomotion in rough terrain requires accurate foot placement, collision avoidance, and planning of the underactuated dynamics of the system. Reliably optimizing for such motions and interactions in the presence of imperfect and often incomplete perceptive information is challenging. We present a complete perception, planning, and control pi...

Robot-assisted neurorehabilitation requires trajectories between arbitrary poses in the patient's range of motion. Data-driven optimization methods, such as Learning by Demonstration, are well suited to replicate complex multi-joint movements. However, these methods lack individualization to patient-, robot- and exercise-specific constraints. We pr...

Loco-manipulation planning skills are pivotal for expanding the utility of robots in everyday environments. These skills can be assessed based on a system's ability to coordinate complex holistic movements and multiple contact interactions when solving different tasks. However, existing approaches have been merely able to shape such behaviors with...

Loco-manipulation planning skills are pivotal for expanding the utility of robots in everyday environments. These skills can be assessed on the basis of a system's ability to coordinate complex holistic movements and multiple contact interactions when solving different tasks. However, existing approaches have been merely able to shape such behavior...

Exoskeleton robots found application in neurorehabilitation, telemanipulation, and power augmentation. The human–robot attachment system of an exoskeleton should transmit all the interaction forces while keeping the anatomical and robotic joint axes aligned. Existing attachment concepts were bounding the performance of modern exoskeletons due to in...

Plaster is commonly used in the construction industry to finish walls and ceilings, but the application is labor-intensive and physically strenuous, which motivates the need for automation. We present PLASTR, a receding horizon optimization-based planning algorithm for robotic plaster trowelling. It samples trowelling sequence rollouts from a new p...

Legged robots are designed to perform highly dynamic motions. However, it remains challenging for users to retarget expressive motions onto these complex systems. In this paper, we present a Differentiable Optimal Control (DOC) framework that facilitates the transfer of rich motions from either animals or animations onto these robots. Interfacing w...

The interest in exploring planetary bodies for scientific investigation and in-situ resource utilization is ever-rising. Yet, many sites of interest are inaccessible to state-of-the-art planetary exploration robots because of the robots' inability to traverse steep slopes, unstructured terrain, and loose soil. Additionally, current single-robot app...

The interest in exploring planetary bodies for scientific investigation and in situ resource utilization is ever-rising. Yet, many sites of interest are inaccessible to state-of-the-art planetary exploration robots because of the robots' inability to traverse steep slopes, unstructured terrain, and loose soil. In addition, current single-robot appr...

Special Issue on Autonomy and AI in Robotics. A team of legged robots can efficiently explore unstructured terrains with task-level autonomy. Arm et al. report on a robot team comprising a “scout” that can identify potential scientific targets in an environment, a “hybrid” that collects data from the targets, and a “scientist” that performs in-dept...

Performing agile navigation with four-legged robots is a challenging task due to the highly dynamic motions, contacts with various parts of the robot, and the limited field of view of the perception sensors. In this paper, we propose a fully-learned approach to train such robots and conquer scenarios that are reminiscent of parkour challenges. The...

We developed an exoskeleton for neurorehabilitation that covered all relevant degrees of freedom of the human arm while providing enough range of motion, speed, strength, and haptic-rendering function for therapy of severely affected (e.g., mobilization) and mildly affected patients (e.g., strength and speed). The ANYexo 2.0, uniting these capabili...

Mobile manipulation in robotics is challenging due to the need to solve many diverse tasks, such as opening a door or picking-and-placing an object. Typically, a basic first-principles system description of the robot is available, thus motivating the use of model-based controllers. However, the robot dynamics and its interaction with an object are...

We present Orbit , a unified and modular framework for robot learning powered by Nvidia Isaac Sim. It offers a modular design to easily and efficiently create robotic environments with photo-realistic scenes and high-fidelity rigid and deformable body simulation. With Orbit , we provide a suite of benchmark tasks of varying difficulty– from s...

Natural environments such as forests and grasslands are challenging for robotic navigation because of the false perception of rigid obstacles from high grass, twigs, or bushes. In this work, we propose Wild Visual Navigation (WVN), an online self-supervised learning system for traversability estimation which uses only vision. The system is able to...

Mobile ground robots require perceiving and understanding their surrounding support surface to move around autonomously and safely. The support surface is commonly estimated based on exteroceptive depth measurements, e.g., from LiDARs. However, the measured depth fails to align with the true support surface in the presence of high grass or other pe...

Quasi-static robotic systems and discrete fabrication strategies fall short of the capabilities needed for automating on-site plastering, which involves operating over large spans and maintaining material continuity. This paper presents continuous, mobile Robotic Plaster Spraying (RPS) – a thin-layer, spray-based printing-in-motion technique using...

We present Orbit, a unified and modular framework for robot learning powered by NVIDIA Isaac Sim. It offers a modular design to easily and efficiently create robotic environments with photo-realistic scenes and high-fidelity rigid and deformable body simulation. With Orbit, we provide a suite of benchmark tasks of varying difficulty -- from single-...

This paper surveys recent progress and discusses future opportunities for Simultaneous Localization And Mapping (SLAM) in extreme underground environments. SLAM in subterranean environments, from tunnels, caves, and man-made underground structures on Earth, to lava tubes on Mars, is a key enabler for a range of applications, such as planetary explo...

Modern robotic systems are required to operate in challenging environments, which demand reliable localization under challenging conditions. LiDAR-based localization methods, such as the Iterative Closest Point (ICP) algorithm, can suffer in geometrically uninformative environments that are known to deteriorate point cloud registration performance...

Terrain geometry is, in general, nonsmooth, nonlinear, nonconvex, and, if perceived through a robot-centric visual unit, appears partially occluded and noisy. This article presents the complete control pipeline capable of handling the aforementioned problems in real-time. We formulate a trajectory optimization problem that jointly optimizes over th...

Modern robotic systems are required to operate in challenging environments, which demand reliable localization under challenging conditions. LiDAR-based localization methods, such as the Iterative Closest Point (ICP) algorithm, can suffer in geometrically uninformative environments that are known to deteriorate registration performance and push opt...

Mobile manipulation in robotics is challenging due to the need of solving many diverse tasks, such as opening a door or picking-and-placing an object. Typically, a basic first-principles system description of the robot is available, thus motivating the use of model-based controllers. However, the robot dynamics and its interaction with an object ar...

LiDAR-based localization and mapping is one of the core components in many modern robotic systems due to the direct integration of range and geometry, allowing for precise motion estimation and generation of high quality maps in real-time. Yet, as a consequence of insufficient environmental constraints present in the scene, this dependence on geome...