Feeling the Shape: Active Exploration Behaviors for Object Recognition With a Robotic Hand
提出一种三指机器人手通过主动探索物体位置来减少不确定性的物体识别方法，采用贝叶斯概率感知和熟悉性/新奇性探索行为，实验验证了主动探索策略的准确性。
Autonomous exploration in robotics is a crucial feature to achieve robust and safe systems capable to interact with and recognize their surrounding environment. In this paper, we present a method for object recognition using a three-fingered robotic hand actively exploring interesting object locations to reduce uncertainty. We present a novel probabilistic perception approach with a Bayesian formulation to iteratively accumulate evidence from robot touch. Exploration of better locations for perception is performed by familiarity and novelty exploration behaviors, which intelligently control the robot hand to move toward locations with low and high levels of interestingness, respectively. These are active behaviors that, similar to the exploratory procedures observed in humans, allow robots to autonomously explore locations they believe that contain interesting information for recognition. Active behaviors are validated with object recognition experiments in both offline and real-time modes. Furthermore, the effects of inhibiting the active behaviors are analyzed with a passive exploration strategy. The results from the experiments demonstrate the accuracy of our proposed methods, but also their benefits for active robot control to intelligently explore and interact with the environment.