Perception and Developmental Learning of Affordances in Autonomous Robots
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
The MACS project: an approach to affordance-inspired robot control
Proceedings of the 2006 international conference on Towards affordance-based robot control
Object shape recognition and grasping by five-fingered robotic hand based on E-ANFIS model
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Visual learning of affordance based cues
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
| Hi-index | 0.00 |
When interacting with an object, the possible choices of grasp and manipulation operations are often limited by pick and place constraints.Traditional planning methods are analytical in nature and require geometric models of parts, fixtures, and motions to identify and avoid the constraints. These methods can easily become computationally expensive and are often brittle under model or sensory uncertainty. In contrast, infants do not construct complete models of the objects that they manipulate, but instead appear to incrementally construct models based on interaction with the objects themselves. We propose that robotic pick and place operations can be formulated as prospective behavior and that an intelligent agent can use interaction with the environment to learn strategies which accommodate the constraints based on expected future success.We present experiments demonstrating this technique, and compare the strategies utilized by the agent to the behaviors observed in young children when presented with a similar task.