176 citations · 356 across the 14 of their papers we have counts for
14 papers
Non-Prehensile Throwing: A Reinforcement Learning Perspective
Abdullah Mustafa, Ryo Hanai, Ixchel G. Ramirez-Alpizar +4
Robotic throwing enables fast object transport and extends a robot's reachable workspace beyond traditional pick-and-place. While prehensile (grasp-based) throwing works well for g…
Learning to Predict Contact Force Distributions from Vision Leveraging Object Geometry Priors
Ryo Hanai, Yukiyasu Domaea, Ixchel G. Ramirez-Alpizar +3
Based on vision and prior experience, humans can make rough physical predictions and adjust their manipulation strategies. This paper aims to endow robots with a similar ability. T…
Robust Instant Policy: Leveraging Student's t-Regression Model for Robust In-context Imitation Learning of Robot Manipulation
Hanbit Oh, Andrea M. Salcedo-Vázquez, Ixchel G. Ramirez-Alpizar +1
Imitation learning (IL) aims to enable robots to perform tasks autonomously by observing a few human demonstrations. Recently, a variant of IL, called In-Context IL, utilized off-t…
Visual Imitation Learning of Non-Prehensile Manipulation Tasks with Dynamics-Supervised Models
Abdullah Mustafa, Ryo Hanai, Ixchel Ramirez +4
Unlike quasi-static robotic manipulation tasks like pick-and-place, dynamic tasks such as non-prehensile manipulation pose greater challenges, especially for vision-based control.…
Breaking Down the Barriers: Investigating Non-Expert User Experiences in Robotic Teleoperation in UK and Japan
Florent P Audonnet, Andrew Hamilton, Yakiyasu Domae +2
Robots are being created each year with the goal of integrating them into our daily lives. As such, there is an interest in research in evaluating the trust of humans toward robots…
IMMERTWIN: A Mixed Reality Framework for Enhanced Robotic Arm Teleoperation
Florent P. Audonnet, Ixchel G. Ramirez-Alpizar, Gerardo Aragon-Camarasa
We present IMMERTWIN, a mixed reality framework for enhance robotic arm teleoperation using a closed-loop digital twin as a bridge for interaction between the user and the robotic…
Tactile-based Active Inference for Force-Controlled Peg-in-Hole Insertions
Tatsuya Kamijo, Ixchel G. Ramirez-Alpizar, Enrique Coronado +1
Reinforcement Learning (RL) has shown great promise for efficiently learning force control policies in peg-in-hole tasks. However, robots often face difficulties due to visual occl…
Force Map: Learning to Predict Contact Force Distribution from Vision
Ryo Hanai, Yukiyasu Domae, Ixchel G. Ramirez-Alpizar +2
When humans see a scene, they can roughly imagine the forces applied to objects based on their experience and use them to handle the objects properly. This paper considers transfer…
Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning Study
Cristian C. Beltran-Hernandez, Damien Petit, Ixchel G. Ramirez-Alpizar +1
The Reinforcement Learning (RL) paradigm has been an essential tool for automating robotic tasks. Despite the advances in RL, it is still not widely adopted in the industry due to…
Assembly Planning by Recognizing a Graphical Instruction Manual
Issei Sera, Natsuki Yamanobe, Ixchel G. Ramirez-Alpizar +3
This paper proposes a robot assembly planning method by automatically reading the graphical instruction manuals design for humans. Essentially, the method generates an Assembly Tas…
Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach
Cristian C. Beltran-Hernandez, Damien Petit, Ixchel G. Ramirez-Alpizar +1
Industrial robot manipulators are playing a more significant role in modern manufacturing industries. Though peg-in-hole assembly is a common industrial task which has been extensi…
Team O2AS at the World Robot Summit 2018: An Approach to Robotic Kitting and Assembly Tasks using General Purpose Grippers and Tools
Felix von Drigalski, Chisato Nakashima, Yoshiya Shibata +12
We propose a versatile robotic system for kitting and assembly tasks which uses no jigs or commercial tool changers. Instead of specialized end effectors, it uses its two-finger gr…
Learning Force Control for Contact-rich Manipulation Tasks with Rigid Position-controlled Robots
Cristian Camilo Beltran-Hernandez, Damien Petit, Ixchel G. Ramirez-Alpizar +4
Reinforcement Learning (RL) methods have been proven successful in solving manipulation tasks autonomously. However, RL is still not widely adopted on real robotic systems because…
Tool Exchangeable Grasp/Assembly Planner
Kensuke Harada, Kento Nakayama, Weiwei Wan +3
This paper proposes a novel assembly planner for a manipulator which can simultaneously plan assembly sequence, robot motion, grasping configuration, and exchange of grippers. Our…