Beyond Games: Bringing Exploration to Robots in Real-world

Exploration has been a long standing problem in both model-based and model-free learning methods for sensorimotor control. While there has been major advances over the years, most of these successes have been demonstrated in either video games or simulation environments. This is primarily because the rewards (even the intrinsic ones) are non-differentiable since they are function of the environment (which is a black-box). In this paper, we focus on the policy optimization aspect of the intrinsic reward function. Specifically, by using a local approximation, we formulate intrinsic reward as a differentiable function so as to perform policy optimization using likelihood maximization -- much like supervised learning instead of reinforcement learning. This leads to a significantly sample efficient exploration policy. Our experiments clearly show that our approach outperforms both on-policy and off-policy optimization approaches like REINFORCE and DQN respectively. But most importantly, we are able to implement an exploration policy on a robot which learns to interact with objects completely from scratch just using data collected via the differentiable exploration module. See project videos at https://doubleblindICLR.github.io/robot-exploration/