  There has been significant recent work on data-driven algorithms for learning
general-purpose grasping policies. However, these policies can consistently
fail to grasp challenging objects which are significantly out of the
distribution of objects in the training data or which have very few high
quality grasps. Motivated by such objects, we propose a novel problem setting,
Exploratory Grasping, for efficiently discovering reliable grasps on an unknown
polyhedral object via sequential grasping, releasing, and toppling. We
formalize Exploratory Grasping as a Markov Decision Process, study the
theoretical complexity of Exploratory Grasping in the context of reinforcement
learning and present an efficient bandit-style algorithm, Bandits for Online
Rapid Grasp Exploration Strategy (BORGES), which leverages the structure of the
problem to efficiently discover high performing grasps for each object stable
pose. BORGES can be used to complement any general-purpose grasping algorithm
with any grasp modality (parallel-jaw, suction, multi-fingered, etc) to learn
policies for objects in which they exhibit persistent failures. Simulation
experiments suggest that BORGES can significantly outperform both
general-purpose grasping pipelines and two other online learning algorithms and
achieves performance within 5% of the optimal policy within 1000 and 8000
timesteps on average across 46 challenging objects from the Dex-Net adversarial
and EGAD! object datasets, respectively. Initial physical experiments suggest
that BORGES can improve grasp success rate by 45% over a Dex-Net baseline with
just 200 grasp attempts in the real world. See https://tinyurl.com/exp-grasping
for supplementary material and videos.
