5 citations · 5 across the 9 of their papers we have counts for
9 papers
Domains as Objectives: Domain-Uncertainty-Aware Policy Optimization through Explicit Multi-Domain Convex Coverage Set Learning
Wendyam Eric Lionel Ilboudo, Taisuke Kobayashi, Takamitsu Matsubara
The problem of uncertainty is a feature of real world robotics problems and any control framework must contend with it in order to succeed in real applications tasks. Reinforcement…
Task-priority Intermediated Hierarchical Distributed Policies: Reinforcement Learning of Adaptive Multi-robot Cooperative Transport
Yusei Naito, Tomohiko Jimbo, Tadashi Odashima +1
Multi-robot cooperative transport is crucial in logistics, housekeeping, and disaster response. However, it poses significant challenges in environments where objects of various we…
Leveraging Demonstrator-perceived Precision for Safe Interactive Imitation Learning of Clearance-limited Tasks
Hanbit Oh, Takamitsu Matsubara
Interactive imitation learning is an efficient, model-free method through which a robot can learn a task by repetitively iterating an execution of a learning policy and a data coll…
Incipient Slip Detection by Vibration Injection into Soft Sensor
Naoto Komeno, Takamitsu Matsubara
In robotic manipulation, preventing objects from slipping and establishing a secure grip on them is critical. Successful manipulation requires tactile sensors that detect the micro…
Reinforcement Learning of Action and Query Policies with LTL Instructions under Uncertain Event Detector
Wataru Hatanaka, Ryota Yamashina, Takamitsu Matsubara
Reinforcement learning (RL) with linear temporal logic (LTL) objectives can allow robots to carry out symbolic event plans in unknown environments. Most existing methods assume tha…
Deep Segmented DMP Networks for Learning Discontinuous Motions
Edgar Anarossi, Hirotaka Tahara, Naoto Komeno +1
Discontinuous motion which is a motion composed of multiple continuous motions with sudden change in direction or velocity in between, can be seen in state-aware robotic tasks. Suc…