Contents:
  Videos
  Slides
  Public Deliverables

		Videos Affordance cueing for a non-liftable test object, involving computational perception units. video format: avi; © Joanneum Research Affordance cueing for a liftable test object, involving computational perception units. video format: mp4; © Joanneum Research Reinforcement learning for affordance perception. video format: avi; © Joanneum Research Reinforcement learning for affordance perception. video format: mp4; © Joanneum Research VOCUS-RT attention system and mean shift filtering for tracking a non-occluded blue test object simultaneously in L and R camera image. video format: avi; © FhG/Fraunhofer IAIS VOCUS-RT attention system and mean shift filtering for tracking a yellow test object simultaneously in L and R camera image, where the object is partially occluded. video format: avi; © FhG/Fraunhofer IAIS KURT3D in simulator MACSim, training for liftability affordance. video format: mpeg; © METU-KOVAN KURT3D in simulator MACSim, scanning test objects. video format: mpeg; © METU-KOVAN KURT3D in simulator MACSim, acting upon traversability affordance. Here, traversability includes pushing pushable objects out of the way. video format: mpeg; © METU-KOVAN KURT3D in real world, acting upon traversability affordance. It applies the same perceptual routines that have been applied in the previous video in MACSim, and that have been trained in the simulator only. video format: mov (Quicktime); © METU-KOVAN KURT3D in simulator MACSim, grounding the lift operator that is employed in goal-oriented behavior. video format: xvid; © University of Osnabrueck KURT3D in simulator MACSim, performing action planning and plan execution. video format: xvid; © University of Osnabrueck KURT3D in simulator MACSim, performing action planning and plan execution with an erroneous world model. video format: xvid; © University of Osnabrueck KURT3D in real world (demonstrator scenario), executing the same plan as shown in the simulator video 12. video format: divx; © FhG/Fraunhofer IAIS Back to top
		Slides from MACS Final Review Overview of MACS project achievements (FhG/IAIS) State of the art in affordance-related research (METU) Perception of Affordances (Joanneum Research) Visual Attention for Exploration (FhG/IAIS) Learning of Affordances (OFAI) Reinforcement Learning for Affordance Perception (Joanneum Research) Alternative Learning and Planning Approach (METU) Planning with Affordances (University of Osnabrück) Back to top
		Public deliverables (PDF) D0.1.7 Publishable Final Activity Report D1.1.1 Specification of SW development environment D1.1.2 Specification of module interfaces D1.1.3 Implementation of SW development environment D1.2.1 Simulation of KURT2 Platform D1.3.1 Integrated implementation of reference control system D2.1.1 Identification of architectural requirements of an affordance-based control D2.2.1 Evaluation of existing control architectures for using affordances D2.2.2 Development of an affordance-based control architecture D2.3.1 Implementation of the affordance-based control architecture D2.3.2 A specification for a propositional planner and its interface to the MACS Execution Control Module D3.1.1 Top-down and bottom-up symbol grounding D3.1.2 Affordance recognition from visual cues D3.1.3 Saliency detection with visual attention D3.1.4 Prototypical affordance based object detection for MACS scenario D3.2.1 Multi-sensor affordance recognition D3.3.1 Prototypical sensormotor based affordance recognition D3.3.2 Sensorimotor decision making and affordance recognition D4.1.1 A conference or journal article summarizing the results of task 4.1. (Survey of affordance-related research) D4.2.1+D4.3.1 Combined deliverable: Tentative Proposal for a Formal Theory of Affordances Tentative Proposal for an Affordance Support Architecture Prototype: Affordance-Based Motion Planner D4.3.2 A software prototype for affordance support D4.3.3 A software prototype of the propositional MACS planning module D4.4.1 A software prototype for an affordance monitoring module with empirical testing using various MACS robotics platforms D4.4.3 An evaluation of the MACS planning module in the context of the MACS architecture D4.4.4 Submission of a conference or journal article describing the results of D4.3.4 and D4.4.3 D5.1.1 Overview of existing affordance learning approaches D5.2.1 Implementation of unsupervised and reinforcement learning algorithms D5.3.1 Robotic learning architecture that can be taught by manually putting the robot through action sequences D5.3.2 Robotic learning architecture capable of autonomously segment action sequences into affordances D5.3.3 Robot prototype learning affordances through self-experience V2 D5.4.2 Prototypical software for representing and learning visual affordance support D5.4.5 Outlook towards affordance usage observation and imitation D6.1.1 Specification of final demonstrator D6.2.1 Simulation model of final demonstrator scenario D6.3.1 Physical robot demonstrator and scenario D6.4.1 Report on experiment design D6.4.2 Report on experimental results in simulator D6.4.3 Report on experimental results in demonstrator D6.5.2 Industry Day Back to top