Accelerated Sensorimotor Learning of Compliant Movement Primitives

Phase-Synchronized Learning of Periodic Compliant Movement Primitives (P-CMPs)

“…In this section we describe the simulations used to compare the P-AFO phase and frequency synchronization performance with the original AFO (Petrič et al, 2011); and two examples of P-CMPs applications with real-world robots, i.e., Kuka LWR-4 and Franka Emika Panda. Note that stability proofs of CMPs system and the AFO systems were already shown in the above-mentioned research (Nakanishi and Schaal, 2004;Petrič et al, 2011Petrič et al, , 2018Deniša et al, 2016). We therefore focused the evaluation on the system improvements and innovations.…”

“…The literature related to modulation and generalization of dynamic parameters is even more sparse. Besides already mentioned (Calinon et al, 2013;Deniša et al, 2013;Petrič et al, 2018), modulation, and generalization of dynamic parameters, such as forces and torques, was also researched in Gams et al (2015), where a statistical generalization was used on force-based coupling terms. However, their approach was limited, since it requires user interaction to generate new database entries.…”

“…As a consequence, the application of CMPs during learning was limited and potentially dangerous when interacting with the environment or humans. To mitigate this issue, an approach using autonomously learning of torque profiles while using compliant robot behavior, i.e., low feed-back gains, was introduced in Petrič et al ( 2018 ). However, the approach was not suitable for periodic tasks.…”

“…The P-AFO is an incremental improvement of AFO proposed in Petrič et al (2011), which guaranties unambiguous frequency and phase synchronization to an arbitrary input signal, which is crucial for P-CMPs. Furthermore, the P-CMPs is a periodic extension of CMPs proposed in Deniša et al (2016) and Petrič et al (2018). Here the kinematic trajectory is encoded in Periodic Dynamic Movement Primitives (Gams et al, 2009) and the corresponding task-specific dynamics with Periodic Torque Primitives (P-TPs).…”

“…In Deniša et al ( 2016 ) TPs were obtained through execution of the desired movement trajectories using high-gain feedback control. This limitation was mitigated in Petrič et al ( 2018 ), where TPs were learned iteratively until the error of compliant tracking was reduced below a predefined threshold.…”