Object Affordance Driven Inverse Reinforcement Learning Through Conceptual Abstraction and Advice

Paladyn: Journal of Behavioral Robotics

Hazarika Shyamanta M.¹  Bhattacharyya Rupam² 

[1] Biomimetic and Cognitive Robotics Lab, Department of Mechanical Engineering, Indian Institute of Technology Guwahati,Assam, India;Department of CSE, Indian Institute of Information Technology Bhagalpur,Bihar, India;

关键词: inverse reinforcement learning;    object affordance;    human intent recognition;    mdp;   

DOI  :  10.1515/pjbr-2018-0021

来源: DOAJ

【 摘 要 】

Within human Intent Recognition (IR), a popular approach to learning from demonstration is Inverse Reinforcement Learning (IRL). IRL extracts an unknown reward function from samples of observed behaviour. Traditional IRL systems require large datasets to recover the underlying reward function. Object affordances have been used for IR. Existing literature on recognizing intents through object affordances fall short of utilizing its true potential. In this paper, we seek to develop an IRL system which drives human intent recognition along with the capability to handle high dimensional demonstrations exploiting the capability of object affordances. An architecture for recognizing human intent is presented which consists of an extended Maximum Likelihood Inverse Reinforcement Learning agent. Inclusion of Symbolic Conceptual Abstraction Engine (SCAE) along with an advisor allows the agent to work on Conceptually Abstracted Markov Decision Process. The agent recovers object affordance based reward function from high dimensional demonstrations. This function drives a Human Intent Recognizer through identification of probable intents. Performance of the resulting system on the standard CAD-120 dataset shows encouraging result.

【 授权许可】

Unknown