We address two domains of skill transfer problems encountered by an autonomous robot: within-domain adaptation and cross-domain transfer. Our aim is to provide skill representations which enable transfer in each problem classification. As such, we explore two approaches to skill representation which address each problem classification separately. The first representation, based on mimicking, encodes the full demonstration and is well suited for within-domain adaptation. The second representation is based on imitation and serves to encode a set of key points along the trajectory, which represent the goal points most relevant to the successful completion of the skill. This representation enables both within-domain and cross-domain transfer. A planner is then applied to these constraints, generating a domain-specific trajectory which addresses the transfer task.
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Ashok Goel: CogSci 2022
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XPrize has selected Georgia Tech’s Veritas team for the round of 10 teams in the Digital Learning Challenge