ICRA 2015 Workshop on Sensorimotor Learning


The workshop is dedicated to recent advances in sensorimotor learning for robotics. The development of robots that are able to learn models of themselves and their environments has long been a goal in the robotics, machine learning, and AI communities. However, most current approaches to robot sensing and control are based on strong prior assumptions, which make them brittle to unmodeled dynamics and unexpected changes in the robot body or the environment. Advances in machine learning, including “deep learning”, nonparametric modeling and inference, and reinforcement learning have recently experienced success in deriving models and policies directly from data. For example, in computer vision, deep learning methods, which learn “everything” from data, including low-level features and intermediate representations, have surpassed traditional approaches in accuracy on problems such as object detection and classification. However, incorporating modern machine learning techniques into real-world sensorimotor systems is still challenging. Most real-world sensorimotor control problems are situated in continuous or high-dimensional environments and require real-time interaction, which can be problematic for classical learning techniques. In order to overcome these difficulties, the modeling, learning, and planning components of a fully adaptive decision making system may need significant modifications. This workshop’s goal is to foster discussion on these issues, especially with the participation of the machine learning and computational biology community.

High-level questions to be addressed include, but are not limited to:

13:20‑13:30Introductory Remarks
13:30‑14:15Ben Kuipers (UMich)"Bootstrap Learning of Real-World Semantics".
14:15‑14:35Fabio Bonsignorio (SSSUP)An approach to sensory-motor learning based on information driven self-organization and Lie groups
14:35‑15:00Johannes A. Stork (KTH)"Semantic interpretable PSRs"
15:00‑15:30break
15:30‑16:00Sergey Levine (UCB)Deep Sensorimotor Learning
16:00‑16:15Chelsea Finn (UCB)“End-to-End Training of Deep Visuomotor Policies”
16:15‑16:35Martin Llofriu (USF)"Bio-Inspired Multi-Scale Representation for Navigation Learning"
16:35‑17:05Russ Salakhutdinov (U Toronto)
17:05‑exhaustionDiscussion

Organizers