539 citations · 2k across the 41 of their papers we have counts for
8 papers · 1 filter
Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning
Kevin Sebastian Luck, Heni Ben Amor, Roberto Calandra
Humans and animals are capable of quickly learning new behaviours to solve new tasks. Yet, we often forget that they also rely on a highly specialized morphology that co-adapted wi…
Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning
Tianyu Li, Nathan Lambert, Roberto Calandra +2
Learning to locomote to arbitrary goals on hardware remains a challenging problem for reinforcement learning. In this paper, we present a hierarchical learning framework that impro…
Data-efficient Learning of Morphology and Controller for a Microrobot
Thomas Liao, Grant Wang, Brian Yang +4
Robot design is often a slow and difficult process requiring the iterative construction and testing of prototypes, with the goal of sequentially optimizing the design. For most rob…
Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots
Dieter Büchler, Roberto Calandra, Jan Peters
High-speed and high-acceleration movements are inherently hard to control. Applying learning to the control of such motions on anthropomorphic robot arms can improve the accuracy o…
Manipulation by Feel: Touch-Based Control with Deep Predictive Models
Stephen Tian, Frederik Ebert, Dinesh Jayaraman +4
Touch sensing is widely acknowledged to be important for dexterous robotic manipulation, but exploiting tactile sensing for continuous, non-prehensile manipulation is challenging.…
Learning to Identify Object Instances by Touch: Tactile Recognition via Multimodal Matching
Justin Lin, Roberto Calandra, Sergey Levine
Much of the literature on robotic perception focuses on the visual modality. Vision provides a global observation of a scene, making it broadly useful. However, in the domain of ro…