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 control scheme and is expensive, and kinesthetic teaching suffers from visual distractions from human intervention. This research proposes a new master-to-robot (M2R) policy transfer system that does not require robots for teaching force feedback-based manipulation tasks. The human directly demonstrates a task using a controller. This controller resembles the kinematic parameters of the robot arm and uses the same end-effector with force/torque (F/T) sensors to measure the force feedback. Using this controller, the operator can feel force feedback without a bilateral system. The proposed method can overcome domain gaps between the master and robot using gaze-based imitation learning and a simple calibration method. Furthermore, a Transformer is applied to infer policy from F/T sensory input. The proposed system was evaluated on a bottle-cap-opening task that requires force feedback.

As robots become more widely available outside industrial settings, the need for reliable object grasping and manipulation is increasing. In such environments, robots must be able to grasp and manipulate novel objects in various situations. This paper presents GraspCaps, a novel architecture based on Capsule Networks for generating per-point 6D grasp configurations for familiar objects. GraspCaps extracts a rich feature vector of the objects present in the point cloud input, which is then used to generate per-point grasp vectors. This approach allows the network to learn specific grasping strategies for each object category. In addition to GraspCaps, the paper also presents a method for generating a large object-grasping dataset using simulated annealing. The obtained dataset is then used to train the GraspCaps network. Through extensive experiments, we evaluate the performance of the proposed approach, particularly in terms of the success rate of grasping familiar objects in challenging real and simulated scenarios. The experimental results showed that the overall object-grasping performance of the proposed approach is significantly better than the selected baseline. This superior performance highlights the effectiveness of the GraspCaps in achieving successful object grasping across various scenarios.