8 citations · 15 across the 13 of their papers we have counts for
22 papers · 1 filter
Real-World Reinforcement Learning with MPC Scaffolding for Dexterous Manipulation
Emek Barış Küçüktabak, Karankumar Patel, Zhaodong Yang +3
Real-world reinforcement learning (RL) offers a promising route to dexterous manipulation policies that can adapt directly from physical interaction, but learning is hindered by in…
Primitive-Informed Sampling-Based MPC for Multi-Fingered Dexterous Manipulation
Emek Barış Küçüktabak, Karankumar Patel, Jinda Cui +3
We present a primitive-informed sampling-based model predictive control (MPC) framework for multi-fingered dexterous manipulation. Sampling-based MPC avoids the need for gradients…
IK Seed Generator for Dual-Arm Human-like Physicality Robot with Mobile Base
Jun Takamatsu, Atsushi Kanehira, Kazuhiro Sasabuchi +2
Robots are strongly expected as a means of replacing human tasks. If a robot has a human-like physicality, the possibility of replacing human tasks increases. In the case of househ…
RL-Driven Data Generation for Robust Vision-Based Dexterous Grasping
Atsushi Kanehira, Naoki Wake, Kazuhiro Sasabuchi +2
This work presents reinforcement learning (RL)-driven data augmentation to improve the generalization of vision-action (VA) models for dexterous grasping. While real-to-sim-to-real…
A Taxonomy of Self-Handover
Naoki Wake, Atsushi Kanehira, Kazuhiro Sasabuchi +2
Self-handover, transferring an object between one's own hands, is a common but understudied bimanual action. While it facilitates seamless transitions in complex tasks, the strateg…
Plan-and-Act using Large Language Models for Interactive Agreement
Kazuhiro Sasabuchi, Naoki Wake, Atsushi Kanehira +2
Recent large language models (LLMs) are capable of planning robot actions. In this paper, we explore how LLMs can be used for planning actions with tasks involving situational huma…