156 citations · 246 across the 43 of their papers we have counts for
45 papers
Experience-Based Feasibility-Aware Generative Adversarial Imitation from Observation under Embodiment Mismatch
Yoshiki Takebayashi, Giovanni Perantoni, Hikaru Sasaki +2
With the increasing use of robot-free demonstration interfaces that provide state trajectories without action labels, imitation from observation has become a promising approach for…
Feasibility-aware Imitation Learning from Observation with Multimodal Feedback
Kei Takahashi, Hikaru Sasaki, Takamitsu Matsubara
Imitation learning frameworks that learn robot control policies from demonstrators' motions via hand-mounted demonstration interfaces have attracted increasing attention. However,…
DAPPER: Discriminability-Aware Policy-to-Policy Preference-Based Reinforcement Learning for Query-Efficient Robot Skill Acquisition
Yuki Kadokawa, Jonas Frey, Takahiro Miki +2
Preference-based Reinforcement Learning (PbRL) enables policy learning through simple queries comparing trajectories from a single policy. While human responses to these queries ma…
Feasibility-aware Imitation Learning from Observations through a Hand-mounted Demonstration Interface
Kei Takahashi, Hikaru Sasaki, Takamitsu Matsubara
Imitation learning through a demonstration interface is expected to learn policies for robot automation from intuitive human demonstrations. However, due to the differences in huma…
Composite Gaussian Processes Flows for Learning Discontinuous Multimodal Policies
Shu-yuan Wang, Hikaru Sasaki, Takamitsu Matsubara
Learning control policies for real-world robotic tasks often involve challenges such as multimodality, local discontinuities, and the need for computational efficiency. These chall…
Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications
Wataru Hatanaka, Ryota Yamashina, Takamitsu Matsubara
Symbolic task representation is a powerful tool for encoding human instructions and domain knowledge. Such instructions guide robots to accomplish diverse objectives and meet const…
Cutting Sequence Diffuser: Sim-to-Real Transferable Planning for Object Shaping by Grinding
Takumi Hachimine, Jun Morimoto, Takamitsu Matsubara
Automating object shaping by grinding with a robot is a crucial industrial process that involves removing material with a rotating grinding belt. This process generates removal res…
Cooperative Grasping and Transportation using Multi-agent Reinforcement Learning with Ternary Force Representation
Ing-Sheng Bernard-Tiong, Yoshihisa Tsurumine, Ryosuke Sota +2
Cooperative grasping and transportation require effective coordination to complete the task. This study focuses on the approach leveraging force-sensing feedback, where robots use…
Robust Iterative Value Conversion: Deep Reinforcement Learning for Neurochip-driven Edge Robots
Yuki Kadokawa, Tomohito Kodera, Yoshihisa Tsurumine +2
A neurochip is a device that reproduces the signal processing mechanisms of brain neurons and calculates Spiking Neural Networks (SNNs) with low power consumption and at high speed…
Unsupervised Neural Motion Retargeting for Humanoid Teleoperation
Satoshi Yagi, Mitsunori Tada, Eiji Uchibe +3
This study proposes an approach to human-to-humanoid teleoperation using GAN-based online motion retargeting, which obviates the need for the construction of pairwise datasets to i…
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…
Learning to Shape by Grinding: Cutting-surface-aware Model-based Reinforcement Learning
Takumi Hachimine, Jun Morimoto, Takamitsu Matsubara
Object shaping by grinding is a crucial industrial process in which a rotating grinding belt removes material. Object-shape transition models are essential to achieving automation…
Disturbance Injection under Partial Automation: Robust Imitation Learning for Long-horizon Tasks
Hirotaka Tahara, Hikaru Sasaki, Hanbit Oh +2
Partial Automation (PA) with intelligent support systems has been introduced in industrial machinery and advanced automobiles to reduce the burden of long hours of human operation.…
Generalized Munchausen Reinforcement Learning using Tsallis KL Divergence
Lingwei Zhu, Zheng Chen, Matthew Schlegel +1
Many policy optimization approaches in reinforcement learning incorporate a Kullback-Leilbler (KL) divergence to the previous policy, to prevent the policy from changing too quickl…
Learning Locally, Communicating Globally: Reinforcement Learning of Multi-robot Task Allocation for Cooperative Transport
Kazuki Shibata, Tomohiko Jimbo, Tadashi Odashima +2
We consider task allocation for multi-object transport using a multi-robot system, in which each robot selects one object among multiple objects with different and unknown weights.…
Deep reinforcement learning of event-triggered communication and consensus-based control for distributed cooperative transport
Kazuki Shibata, Tomohiko Jimbo, Takamitsu Matsubara
In this paper, we present a solution to a design problem of control strategies for multi-agent cooperative transport. Although existing learning-based methods assume that the numbe…
Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies for Robot Manipulation
Hanbit Oh, Hikaru Sasaki, Brendan Michael +1
Humans demonstrate a variety of interesting behavioral characteristics when performing tasks, such as selecting between seemingly equivalent optimal actions, performing recovery ac…
Deep Koopman with Control: Spectral Analysis of Soft Robot Dynamics
Naoto Komeno, Brendan Michael, Katharina Küchler +2
Soft robots are challenging to model and control as inherent non-linearities (e.g., elasticity and deformation), often requires complex explicit physics-based analytical modeling (…
Physically Consistent Preferential Bayesian Optimization for Food Arrangement
Yuhwan Kwon, Yoshihisa Tsurumine, Takeshi Shimmura +2
This paper considers the problem of estimating a preferred food arrangement for users from interactive pairwise comparisons using Computer Graphics (CG)-based dish images. As a foo…
Goal-Aware Generative Adversarial Imitation Learning from Imperfect Demonstration for Robotic Cloth Manipulation
Yoshihisa Tsurumine, Takamitsu Matsubara
Generative Adversarial Imitation Learning (GAIL) can learn policies without explicitly defining the reward function from demonstrations. GAIL has the potential to learn policies wi…
Randomized-to-Canonical Model Predictive Control for Real-world Visual Robotic Manipulation
Tomoya Yamanokuchi, Yuhwan Kwon, Yoshihisa Tsurumine +3
Many works have recently explored Sim-to-real transferable visual model predictive control (MPC). However, such works are limited to one-shot transfer, where real-world data must b…