Learning variable impedance control

By Jonas Buchli, Freek Stulp, Evangelos A. Theodorou, Stefan Schaal. 2011

397 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cognitive Neuroscience

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Citations in the corpus

Reinforcement learning in robotics: A survey (2013, methodology): “…and Schaal, 2008c,b), reaching tasks (Guenter et al., 2007; Bitzer et al., 2010), ball in a cup (Kober and Peters, 2009), flipping a light switch (Buchli et al., 2011), playing pool and manipulating a box (Pastor et al., 2011), and opening a door and picking up objects (Kalakrishnan et al.,…”
Learning object-level impedance control for robust grasping and dexterous manipulation (2014, methodology): “In [18], the authors accomplished a variable impedance controller with a modelfree, sample-based reinforcement learning method.”
Kernelized movement primitives (2017, neutral): “For instance, a variant of policy improvement with path integrals (Buchli et al. 2011) was employed to optimize the movement pattern of DMP (Stulp and Sigaud 2013).”
Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks (2019, methodology): “The parameters of these functions, κ , can be made part of the policy action space so that the agent has full controllability on the manipulation behavior [4].”
A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms (2019, background): “values for inputs to the controller, the policy may also output additional values for adapting the controller. In particular, a policy may dene dierent controller gains as part of its action space (Buchli et al., 2011; Ren et al., 2018). In this manner, the policy can make a robot more compliant or ”
Imitation Learning (2017, methodology): “This led to a series of work that utilizes the dynamic system in Ijspeert et al. [2002b] to learn from demonstrations and subsequently use reinforcement learning for self-improvement [Kober and Peters 2010, 2014; Buchli et al. 2011; Pastor et al. 2011].”
Policy Improvement : Between Black-Box Optimization and Episodic Reinforcement Learning (2013, background): “…speed and robustness of policy improvement methods has increased dramatically, such that they are now able to learn a variety of challenging robotic tasks (Theodorou et al., 2010; Rückstiess et al., 2010b; Tamosiumaite et al., 2011; Kober & Peters, 2011; Buchli et al., 2011; Stulp et al., 2012).”