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Karlsruhe Institute of Technology / TimeStorm Proyect – Armar III Humanoid Robotic preparing scrambled eggs together with a human / Time Perception and Time-aware Planning

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Time Perception and Time-aware Planning in Humanoid Robotics

This video shows an integrated demonstration of central results of the TimeStorm project. In the example task of preparing scrambled eggs together with a human, the robot ARMAR-III uses its time-aware planner to plan and execute the necessary actions towards its goal. The demonstration highlights the aspects of the realization of time perception in robot systems. The scenario integrates several scientific methods developed in the project: – Time aware planning with stressed actions – Deep episodic memory – Temporal action scaling based on psychological feedback
TimeStorm Project

TimeStorm aims at equipping artificial systems with humanlike cognitive skills that benefit from the flow of time by shifting the focus of human-machine confluence to the temporal, short- and long-term aspects of symbiotic interaction. The integrative pursuit of research and technological developments in time perception will contribute significantly to ongoing efforts in deciphering the relevant brain circuitry and will also give rise to innovative implementations of artifacts with profoundly enhanced cognitive capacities. TimeStorm promotes time perception as a fundamental capacity of autonomous living biological and computational systems that plays a key role in the development of intelligence. In particular, time is important for encoding, revisiting and exploiting experiences (knowing), for making plans to accomplish timely goals at certain moments (doing), for maintaining the identity of self over time despite changing contexts (being).

The main role of KIT in TimeStorm is to investigate the temporal information in the perception and execution of manipulation actions and to integrate time processing mechanisms in humanoid robots. In particular, we investigate how semantic representation (top-down) and hierarchical segmentation (bottom-up) of human demonstrations based on spatio-temporal object interactions can be combined to facilitate generalization of action durations. This would allow a robot to scale perceived and learnt temporal information of an action in order to perform the same and other actions with various temporal lengths.

Source video, image and text: HumanoidRobots
Source web text TimeStorm Project: TimeStorm

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