70 citations · 213 across the 23 of their papers we have counts for
7 papers · 1 filter
Intentionally-underestimated Value Function at Terminal State for Temporal-difference Learning with Mis-designed Reward
Taisuke Kobayashi
Robot control using reinforcement learning has become popular, but its learning process generally terminates halfway through an episode for safety and time-saving reasons. This stu…
Real-time Sampling-based Model Predictive Control based on Reverse Kullback-Leibler Divergence and Its Adaptive Acceleration
Taisuke Kobayashi, Kota Fukumoto
Sampling-based model predictive control (MPC) has the potential for use in a wide variety of robotic systems. However, its unstable updates and poor convergence render it unsuitabl…
L2C2: Locally Lipschitz Continuous Constraint towards Stable and Smooth Reinforcement Learning
Taisuke Kobayashi
This paper proposes a new regularization technique for reinforcement learning (RL) towards making policy and value functions smooth and stable. RL is known for the instability of t…
Latent Representation in Human-Robot Interaction with Explicit Consideration of Periodic Dynamics
Taisuke Kobayashi, Shingo Murata, Tetsunari Inamura
This paper presents a new data-driven framework for analyzing periodic physical human-robot interaction (pHRI) in latent state space. To elaborate human understanding and/or robot…
Sample-efficient Gear-ratio Optimization for Biomechanical Energy Harvester
Taisuke Kobayashi, Yutaro Ikawa, Takamitsu Matsubara
The biomechanical energy harvester is expected to harvest the electric energies from human motions. A tradeoff between harvesting energy and keeping the user's natural movements sh…
Mirror-Descent Inverse Kinematics for Box-constrained Joint Space
Taisuke Kobayashi, Takanori Jin
To control humanoid robots, the reference pose of end effector(s) is planned in task space, then mapped into the reference joints by IK. By viewing that problem as approximate quad…
Adaptive and Multiple Time-scale Eligibility Traces for Online Deep Reinforcement Learning
Taisuke Kobayashi
Deep reinforcement learning (DRL) is one promising approach to teaching robots to perform complex tasks. Because methods that directly reuse the stored experience data cannot follo…