Robotics

Coding Challenge Coding Live

Our team contributes to the representations and mechanisms for efficient and cost-effective long-term data management across devices, as well as scene understanding starting from the detection of semantic features, classification of objects, all the way to behavior analysis and intent prediction.

Tracking robot motion via visual and/or inertial cues.

IBM Research scientist Martin Rufli explains cognitive robotics.

Our team is contributing its know-how to a numerous industry-driven projects ranging from dataset/knowledge ingestion and maintenance to the enablement of a seamless context-aware conversation between operator and machine.

A Decentralized Trust-minimized Cloud Robotics Architecture

Within the context of Industry 4.0, manufacturing is gearing away from monolithic assembly lines and going towards flexible, collaborative assembly pods suitable for (mass) customized fabrication.

The application space is represented by a range of modules implementing a specific functionality along the sensethinkact cycle of artificial intelligence.

between hardware and service applications.

We continuously host a range of industrial internships and Masters thesis projects in close collaboration with various robotics labs at ETH Zurich. Just get in touch with us to discuss opportinities.

Geometric 3D reconstruction encompasses techniques for merging individual exteroceptive mainly camera or laser-based sensor scans into a consistent 2D or 3D representation.

Our Vibe implementation is a state-of-the-art visual-inertial tracking and mapping system targeted at robotics, augmented reality and IoT applications.

The image at right illustrates our spatial cognition framework spanning the full stack

A key aspect in this scenario is the seamless yet robust programming of individual work steps (for example by means of demonstrations) within an uncertainty-dominated environment of human design.

Containerized, these functional modules become reliably deployable on diverse infrastructures with minimal dependencies. This makes them readily consumable as services.

Read on to get a glimpse of the many core methods and use-case scenarios we are working on, for which spatial cognition represents a critical enabler.

The physical environment itself is the ultimate user interface between man and machine.

We refer to this emerging area of robotics as spatial cognition. Applications of spatial cognition range from service robotics in domestic, commercial and industrial settings to mixed reality and cognitive IoT.

Tracking describes the process of estimating the egomotion of an agent such as a vehicle, human or robot, for example by fusing the information of visual and inertial sensors attached to it. Over time, this process is inherently affected by drift.

Our research focuses on a range of core methods, that jointly enable novel spatial cognition applications.

We aim to enable IBMs cognitive computer Watson to perceive and reason on a semantic level in response to the unstructured physical world in which it is embedded. This will facilitate services where humans and cognitive systems interact and collaborate in real-time.

Current focus areas comprise multi-sensor tracking, geometric 3D reconstruction and semantic knowledge representation.

We are part of several public-funded research initiatives in the context of connected / automated driving, such as theAutomated Urban Parking and Driving (UP-Drive)andAutopilotprojects.

A Distributed Cloud Robotics Middleware Architecture for Trust-minimized Storage and Computation

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