103 citations · 225 across the 21 of their papers we have counts for
6 papers · 1 filter
FlexLAM: Resolving the Bottleneck Trade-off in Latent Action Learning
Takanori Yoshimoto, Yang Hu, Naruya Kondo +1
Latent actions provide a compact interface between action-free video and downstream decision-making, yet existing Latent Action Models (LAMs) force every transition through a fixed…
Retrieve-Augmented Generation for Speeding up Diffusion Policy without Additional Training
Sodtavilan Odonchimed, Tatsuya Matsushima, Simon Holk +2
Diffusion Policies (DPs) have attracted attention for their ability to achieve significant accuracy improvements in various imitation learning tasks. However, DPs depend on Diffusi…
GenORM: Generalizable One-shot Rope Manipulation with Parameter-Aware Policy
So Kuroki, Jiaxian Guo, Tatsuya Matsushima +7
Due to the inherent uncertainty in their deformability during motion, previous methods in rope manipulation often require hundreds of real-world demonstrations to train a manipulat…
Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning
Hiroki Furuta, Tadashi Kozuno, Tatsuya Matsushima +2
Recently many algorithms were devised for reinforcement learning (RL) with function approximation. While they have clear algorithmic distinctions, they also have many implementatio…
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning
Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno +4
Progress in deep reinforcement learning (RL) research is largely enabled by benchmark task environments. However, analyzing the nature of those environments is often overlooked. In…
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo +2
Most reinforcement learning (RL) algorithms assume online access to the environment, in which one may readily interleave updates to the policy with experience collection using that…