11 accepted papers
Autonomous agents capable of diverse object manipulations should be able to acquire a wide range of manipulation skills with high reusability. Although advances in deep learning have made it increasingly feasible to replicate the dexterity of human teleoperation in robots, generalizing these acquire…
In imitation learning for robotic manipulation, decomposing object manipulation tasks into sub-tasks enables the reuse of learned skills and the combination of learned behaviors to perform novel tasks, rather than simply replicating demonstrated motions. Human gaze is closely linked to hand movement
Deep imitation learning is a promising approach in robotic manipulation, enabling robots to acquire versatile and adaptable skills. In such research, by learning various tasks, robots achieved generality across multiple objects. However, such multi-task robot datasets have mainly focused on single-a…
Deep imitation learning is promising for robot manipulation because it only requires demonstration samples. In this study, deep imitation learning is applied to tasks that require force feedback. However, existing demonstration methods have deficiencies; bilateral teleoperation requires a complex co
Deep imitation learning is a promising approach that does not require hard-coded control rules in autonomous robot manipulation. The current applications of deep imitation learning to robot manipulation have been limited to reactive control based on the states at the current time step. However, futu…
Imitation learning has attracted attention as a method for realizing complex robot control without programmed robot behavior. Meta-imitation learning has been proposed to solve the high cost of data collection and low generalizability to new tasks that imitation learning suffers from. Meta-imitation…
A high-precision manipulation task, such as needle threading, is challenging. Physiological studies have proposed connecting low-resolution peripheral vision and fast movement to transport the hand into the vicinity of an object, and using high-resolution foveated vision to achieve the accurate homi
Deep imitation learning is promising for solving dexterous manipulation tasks because it does not require an environment model and pre-programmed robot behavior. However, its application to dual-arm manipulation tasks remains challenging. In a dual-arm manipulation setup, the increased number of sta…
Segmentation of a compound task with multiple subtasks is crucial for imitation learning. Conventional unsupervised segmentation methods focused on only reproducibility of demonstrations and did not use the property that goal-directed actions rarely occur without intention. In this paper, we propose…
Deep imitation learning enables the learning of complex visuomotor skills from raw pixel inputs. However, this approach suffers from the problem of overfitting to the training images. The neural network can easily be distracted by task-irrelevant objects. In this letter, we use the human gaze measur
Visual occlusions caused by the environment or by the robot itself can be a problem for object recognition during manipulation by a robot hand. Under such conditions, tactile and somatosensory information are useful for object recognition during manipulation. Humans can visualize the appearance of i…