3 accepted papers
We contribute a study benchmarking the performance of multiple motion-based learning from demonstration approaches. Given the number and diversity of existing methods, it is critical that comprehensive empirical studies be performed comparing the relative strengths of these techniques. In particular…
We address the challenge of how a robot can adapt its actions to successfully manipulate objects it has not previously encountered. We introduce Real-time Multisensory Affordance-based Control (RMAC), which enables a robot to adapt existing affordance models using multisensory inputs. We show that u…
In realistic environments, fully specifying a task model such that a robot can perform a task in all situations is impractical. In this work, we present Incremental Task Modification via Corrective Demonstrations (ITMCD), a novel algorithm that allows a robot to update a learned model by making use…