7 papers
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…
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…
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…