Sparse Latent Space Policy Search

         Kevin presented our new paper

                                     “Sparse Latent Space Policy Search”

                                          on the AAAI-16 conference.

Find out more about our research

Machine Learning

Using reinforcement learning, in particular policy search, as a basis, we introduced various methods for skill acquisition in humanoid robots.

Human-Robot Interaction

We introduced new representations such as “Interaction Primitives” and “Correlation-based Interaction Meshes” to enhance HRI.

Grasping and Manipulation

Our research focuses on the application of machine learning and imitation learning techniques to allow robots precise and goal-oriented manipulation.

Robot Autonomy

We are investigating new methodologies that allow robots to autonomously explore their environment and change their goal and objectives.

General News

Dr. Ben Amor invited to talk in the Robotics Institute seminar series at CMU

On March 12, Dr. Ben Amor gave a talk about Human-Robot Interactive Collaboration & Communication in the Robotics Institute seminar series at CMU. Abstract: Autonomous and anthropomorphic robots are poised to play a critical role in manufacturing, healthcare, and the services industry in the near future. However, for this vision to become a reality, robots need to efficiently communicate and interact with their human partners. Rather than traditional remote controls and programming languages, adaptive and transparent techniques for human-robot collaboration are needed. In particular, robots may need to interpret implicit behavioral cues or explicit instructions and, in turn, generate appropriate responses. Dr. Ben Amor presented ongoing work which leverages machine learning (ML), natural language processing, and virtual reality to create different modalities for humans and machines to engage in effortless and natural interactions. To this end, he described Bayesian Interaction Primitives – an approach for motor skill learning and Spatio-temporal modeling in physical human-robot collaboration tasks. He also discussed our recent work on language-conditioned imitation learning and self-supervised learning in interactive tasks. The talk also covered techniques that enable robots to communicate information back to the human partner via mixed reality projections. To demonstrate these techniques, Dr. Ben Amor presented applications in prosthetics, social robotics, and collaborative assembly.

Dr. Ben Amor receives the Fulton Best Teacher Award

Dr. Ben Amor has been selected to receive a Fulton Schools of Engineering Best Teacher Award – Top 5% for the academic year of 2017-2018. Each year, students in the Ira A. Fulton Schools of Engineering submit nominations and teaching evaluations. This information is reviewed by our Quality of Instruction faculty committee, with final selections made based on input by the directors of the six schools under Fulton Schools of Engineering and the Dean of Fulton Schools of Engineering.

Dr. Ben Amor receives NSF CAREER Award

The lab director of the Interactive Robotics Lab, Dr. Heni Ben Amor received the prestigious the National Science Foundation Faculty Early Career Development (CAREER). The NSF CAREER award is most prestigious awards in support of early-career faculty who have the potential to serve as academic role models. Dr. Ben Amor received the award for his seminal work on human robot interaction. More information can be found under: ASU NOW

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.

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.