Sparse Latent Space Policy Search

         Kevin presented our new paper

                                     “Sparse Latent Space Policy Search”

                                          on the AAAI-16 conference.

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General News

The 5 Most Technologically Advanced Robots (For Their Time)


“Heni Ben Amor, assistant professor at Arizona State University, where he leads the ASU Interactive Robotics Laboratory, says some of the most important robots contributing the greatest advancements to the robotics field may not have the big-name recognition of their fictional cousins. But, he adds, they have been instrumental in moving the industry forward.” (Nancy Giges, Independent writer for ASME)

The full article can be found here

GPUs for Deep Learning and Embedded Technologies Workshop @ ASU


We are organizing a joint workshop on “GPUs for Deep Learning and Embedded Technologies” with Nvidia! GPUs, Deep learning and Embedded Systems are a rapidly growing segment of artificial intelligence. They are increasingly used to deliver near-human level accuracy for image classification, voice recognition, natural language processing, sentiment analysis, recommendation engines, and more. Applications areas include facial recognition, scene detection, advanced medical and pharmaceutical research, and autonomous, self-driving vehicles.

Our workshop will introduce students into Deep Learning on GPU’s, more details here:

Lab News

Landmine-clearing Pi-powered C-Turtle


“In an effort to create a robot that can teach itself to navigate different terrains, scientists at Arizona State University have built C-Turtle, a Raspberry Pi-powered autonomous cardboard robot with turtle flippers. This is excellent news for people who live in areas with landmines: C-Turtle is a great alternative to current landmine-clearing robots, since it is much cheaper, and much easier to assemble.” (By Janina Ander)

The full article can be found here

Intention projection featured at the Institution of Mechanical Engineers


The work of our students Ramsundar and Yash are featured at the Institution of Mechanical Engineers. Our “[…] lab has created an augmented-reality approach, where the robot uses a projector to highlight objects it’s about to reach for, or to illuminate the route it’s going to take. “The environment becomes a canvas for the robot to communicate its intent to the human partner” (Amor) […].” (Amit Katwala,

The full article can be found here

New article “ASU Robotics turns to nature for inspiration” about our C-TURTLE at 3TV / CBS 5


Video by ( KPHO Broadcasting Corporation )

“ASU Robotics students have developed a robot that mimics a sea turtle as part of a research project looking at ways to integrate computer science, biology and engineering. The team wanted to come up with the best solution on how to travel over sand. The students settled on a sea turtle as a great option.” (

Full article here

Two new papers on our robot turtle accepted


We have two new papers accepted to RSS and Living Machines 2017.

The submission to RSS, From the Lab to the Desert: Fast Prototyping and Learning of Robot Locomotion, introduces a new methodology which combines quick prototyping and sample-efficient reinforcement learning in order to produce effective locomotion of a sea-turtle inspired robotic platform in a desert environment.

The submission to Living Machines, Bio-inspired Robot Design Considering Load-bearing and Kinematic Ontogeny of Chelonioidea Sea Turtles, explores the effect of biologically-inspired fins on the locomotion of our sea-turtle robot.

New Paper: Sample-Efficient Reinforcement Learning for Robot to Human Handover Tasks


We have a new paper accepted at The Multi-disciplinary Conference on Reinforcement Learning and Decision Making. Trevor will present our new work on learning to hand over objects at the UMICH Ann Arbor. Abstract:
While significant advancements have been made recently in the field of reinforcement learning, relatively little work has been devoted to reinforcement learning in a human context. Learning in the context of a human adds a variety of additional constraints that make the problem more difficult including an increased importance on sample efficiency and the inherent unpredictability of the human counterpart. In this work we used the Sparse Latent Space Policy Search algorithm and a linear-Gaussian trajectory approximator with the objective of learning optimized, understandable trajectories for object handovers between a robot and a human with very high sample-efficiency.

Graduation Spring 2017: Ramsundar Kalpagam Ganesan and Indranil Sur


Our graduate students Ramsundar Kalpagam Ganesan and Indranil Sur graduated with a masters degree in spring 2017. Ramsundar worked on “Mediating Human-Robot Collaboration through Mixed Reality Cues”, utilizing virtual reality to ease complex Human-Robot Interaction. After his graduation he will start at Delphi. Automotive working on autonomous driving. Indranil worked on “Robots that Anticipate Pain: Anticipating Physical Perturbations from Visual Cues through Deep Predictive Models”. His work focused on teaching robots how to anticipate pain to create safer Human-Robot Interaction. He will start soon at SRI in Princeton. We wish them the best for their future career.

About the Lab

This is the website of the Interactive Robotics Laboratory (Ben Amor - Lab) at Arizona State University. We focus on developing novel machine learning techniques that allow robots to physically interact with objects and humans in their environment.