15 citations · 28 across the 8 of their papers we have counts for
8 papers
Catch, Throw, Repeat: Planning for Human-Robot Partner Juggling
Jonathan Rainer Lippert, Kai Ploeger, Abir Chowdhury +3
Dynamic object exchange between humans and robots remains a challenging problem due to uncertainty in perception, timing, and contact-rich interaction. Human-robot juggling represe…
Task-Error Residual Learning for Real-Robot Five-Ball Juggling
Kai Ploeger, Jan Peters, Alap Kshirsagar
For residual learning that refines existing behavior, sample efficiency depends on two things: how much information each rollout returns, and how efficiently the learner uses that…
Beyond the Cascade: Juggling Vanilla Siteswap Patterns
Mario Gomez Andreu, Kai Ploeger, Jan Peters
Being widespread in human motor behavior, dynamic movements demonstrate higher efficiency and greater capacity to address a broader range of skill domains compared to their quasi-s…
Tracking Control for a Spherical Pendulum via Curriculum Reinforcement Learning
Pascal Klink, Florian Wolf, Kai Ploeger +2
Reinforcement Learning (RL) allows learning non-trivial robot control laws purely from data. However, many successful applications of RL have relied on ad-hoc regularizations, such…
Controlling the Cascade: Kinematic Planning for N-ball Toss Juggling
Kai Ploeger, Jan Peters
Dynamic movements are ubiquitous in human motor behavior as they tend to be more efficient and can solve a broader range of skill domains than their quasi-static counterparts. For…
SKID RAW: Skill Discovery from Raw Trajectories
Daniel Tanneberg, Kai Ploeger, Elmar Rueckert +1
Integrating robots in complex everyday environments requires a multitude of problems to be solved. One crucial feature among those is to equip robots with a mechanism for teaching…