1 citations · 1 across the 3 of their papers we have counts for
6 papers
The utility of tactile force to autonomous learning of in-hand manipulation is task-dependent
Romina Mir, Ali Marjaninejad, Francisco J. Valero-Cuevas
Tactile sensors provide information that can be used to learn and execute manipulation tasks. Different tasks, however, might require different levels of sensory information; which…
Autonomous Control of a Tendon-driven Robotic Limb with Elastic Elements Reveals that Added Elasticity can Enhance Learning
Ali Marjaninejad, Jie Tan, Francisco J. Valero-Cuevas
Passive elastic elements can contribute to stability, energetic efficiency, and impact absorption in both biological and robotic systems. They also add dynamical complexity which m…
Simple Kinematic Feedback Enhances Autonomous Learning in Bio-Inspired Tendon-Driven Systems
Ali Marjaninejad, Darío Urbina-Meléndez, Francisco J. Valero-Cuevas
Error feedback is known to improve performance by correcting control signals in response to perturbations. Here we show how adding simple error feedback can also accelerate and rob…
Autonomous Functional Locomotion in a Tendon-Driven Limb via Limited Experience
Ali Marjaninejad, Darío Urbina-Meléndez, Brian A. Cohn +1
Robots will become ubiquitously useful only when they can use few attempts to teach themselves to perform different tasks, even with complex bodies and in dynamical environments. V…
Quantifying and attenuating pathologic tremor in virtual reality
Brian A. Cohn, Dilan D. Shah, Ali Marjaninejad +6
We present a virtual reality (VR) experience that creates a research-grade benchmark in assessing patients with active upper-limb tremor, while simultaneously offering the opportun…
Shapechanger: Environments for Transfer Learning
Sébastien M. R. Arnold, Tsam Kiu Pun, Théo-Tim J. Denisart +1
We present Shapechanger, a library for transfer reinforcement learning specifically designed for robotic tasks. We consider three types of knowledge transfer---from simulation to s…