4 research outputs found
Belief Tree Search for Active Object Recognition
Active Object Recognition (AOR) has been approached as an unsupervised
learning problem, in which optimal trajectories for object inspection are not
known and are to be discovered by reducing label uncertainty measures or
training with reinforcement learning. Such approaches have no guarantees of the
quality of their solution. In this paper, we treat AOR as a Partially
Observable Markov Decision Process (POMDP) and find near-optimal policies on
training data using Belief Tree Search (BTS) on the corresponding belief Markov
Decision Process (MDP). AOR then reduces to the problem of knowledge transfer
from near-optimal policies on training set to the test set. We train a Long
Short Term Memory (LSTM) network to predict the best next action on the
training set rollouts. We sho that the proposed AOR method generalizes well to
novel views of familiar objects and also to novel objects. We compare this
supervised scheme against guided policy search, and find that the LSTM network
reaches higher recognition accuracy compared to the guided policy method. We
further look into optimizing the observation function to increase the total
collected reward of optimal policy. In AOR, the observation function is known
only approximately. We propose a gradient-based method update to this
approximate observation function to increase the total reward of any policy. We
show that by optimizing the observation function and retraining the supervised
LSTM network, the AOR performance on the test set improves significantly.Comment: IROS 201
Active In-Hand Object Recognition on a Humanoid Robot
For any robot, the ability to recognize and manipulate unknown objects is crucial to successfully work in natural environments. Object recognition and categorization is a very challenging problem, as 3-D objects often give rise to ambiguous, 2-D views. Here, we present a perception-driven exploration and recognition scheme for in-hand object recognition implemented on the iCub humanoid robot. In this setup, the robot actively seeks out object views to optimize the exploration sequence. This is achieved by regarding the object recognition problem as a localization problem. We search for the most likely viewpoint position on the viewsphere of all objects. This problem can be solved efficiently using a particle filter that fuses visual cues with associated motor actions. Based on the state of the filter, we can predict the next best viewpoint after each recognition step by searching for the action that leads to the highest expected information gain. We conduct extensive evaluations of the proposed system in simulation as well as on the actual robot and show the benefit of perception-driven exploration over passive, vision-only processes at discriminating between highly similar objects. We demonstrate that objects are recognized faster and at the same time with a higher accuracy