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Jim Mainprice

7 accepted papers

2021

Guest Editorial: Introduction to the Special Issue on Long-Term Human Motion Prediction

Luigi Palmieri, Andrey Rudenko, Jim Mainprice, Marc Hanheide, Alexandre Alahi, Achim J. Lilienthal +1
RA-L 2021

The articles in this special section focus on long term human motion prediction. This represents a key ability for advanced autonomous systems, especially if they operate in densely crowded and highly dynamic environments. In those settings understanding and anticipating human movements is fundament

Cited by 2Source ↗Scholar ↗
2021

Learning to Arbitrate Human and Robot Control using Disagreement between Sub-Policies

Yoojin Oh, Marc Toussaint, Jim Mainprice
IROS 2021poster

In the context of teleoperation, arbitration refers to deciding how to blend between human and autonomous robot commands. We present a reinforcement learning solution that learns an optimal arbitration strategy that allocates more control authority to the human when the robot comes across a decision…

Cited by 11Source ↗Scholar ↗
2021

MoGaze: A Dataset of Full-Body Motions that Includes Workspace Geometry and Eye-Gaze

Philipp Kratzer, Simon Bihlmaier, Niteesh Balachandra Midlagajni, Rohit Prakash, Marc Toussaint, Jim Mainprice
RA-L 2021

As robots become more present in open human environments, it will become crucial for robotic systems to understand and predict human motion. Such capabilities depend heavily on the quality and availability of motion capture data. However, existing datasets of full-body motion rarely include 1) long

Cited by 56Source ↗code ↗Scholar ↗
2020

Prediction of Human Full-Body Movements with Motion Optimization and Recurrent Neural Networks

Philipp Kratzer, Marc Toussaint, Jim Mainprice
ICRA 2020poster

Human movement prediction is difficult as humans naturally exhibit complex behaviors that can change drastically from one environment to the next. In order to alleviate this issue, we propose a prediction framework that decouples short-term prediction, linked to internal body dynamics, and long-term…

Cited by 52Source ↗Scholar ↗
2018

Real-Time Perception Meets Reactive Motion Generation

Daniel Kappler, Franziska Meier, Jan Issac, Jim Mainprice, Cristina Garcia Cifuentes, Manuel Wuthrich +4
RA-L 2018

We address the challenging problem of robotic grasping and manipulation in the presence of uncertainty. This uncertainty is due to noisy sensing, inaccurate models, and hard-to-predict environment dynamics. We quantify the importance of continuous, real-time perception and its tight integration with

Cited by 120Source ↗Scholar ↗
2016

Warping the workspace geometry with electric potentials for motion optimization of manipulation tasks

Jim Mainprice, Nathan Ratliff, Stefan Schaal
IROS 2016poster

In this paper we present motion optimization algorithms for computing manipulation motions in presence of obstacles. Our approach builds a geometric representation of the workspace by constructing Riemannian metrics using electric potentials emanating from the workspace obstacles. Velocity of the ro…

Cited by 20Source ↗Scholar ↗
2015

Predicting human reaching motion in collaborative tasks using Inverse Optimal Control and iterative re-planning

Jim Mainprice, Rafi Hayne, Dmitry Berenson
ICRA 2015poster

To enable safe and efficient human-robot collaboration in shared workspaces, it is important for the robot to predict how a human will move when performing a task. While predicting human motion for tasks not known a priori is very challenging, we argue that single-arm reaching motions for known task…

Cited by 159Source ↗Scholar ↗