Feature-Based Prediction of Trajectories for Socially Compliant Navigation

Kuderer, Markus; Kretzschmar, Henrik; Sprunk, Christoph; Burgard, Wolfram

doi:10.15607/rss.2012.viii.025

Cited by 191 publications

(187 citation statements)

References 25 publications

Supporting

Mentioning

187

Contrasting

Order By: Relevance

“…Anticipation or forecasting future human actions has been the focus of few recent works. Maximum entropy inverse reinforcement learning was used by [48], [49], [50] to obtain a distribution over possible human navigation trajectories from visual data, and also used to model the forthcoming interactions with pedestrians for mobile robots [48], [50]. However, these works focus only on human actions which are limited to navigation trajectories.…”

Section: Related Workmentioning

confidence: 99%

Anticipating Human Activities using Object Affordances for Reactive Robotic Response

Koppula

Saxena

2013

Robotics: Science and Systems IX

159

214

View full text Add to dashboard Cite

Abstract-An important aspect of human perception is anticipation, which we use extensively in our day-to-day activities when interacting with other humans as well as with our surroundings. Anticipating which activities will a human do next (and how) can enable an assistive robot to plan ahead for reactive responses. Furthermore, anticipation can even improve the detection accuracy of past activities. The challenge, however, is two-fold: We need to capture the rich context for modeling the activities and object affordances, and we need to anticipate the distribution over a large space of future human activities. In this work, we represent each possible future using an anticipatory temporal conditional random field (ATCRF) that models the rich spatial-temporal relations through object affordances. We then consider each ATCRF as a particle and represent the distribution over the potential futures using a set of particles. In extensive evaluation on CAD-120 human activity RGB-D dataset, we first show that anticipation improves the state-ofthe-art detection results. We then show that for new subjects (not seen in the training set), we obtain an activity anticipation accuracy (defined as whether one of top three predictions actually happened) of 84.1%, 74.4% and 62.2% for an anticipation time of 1, 3 and 10 seconds respectively. Finally, we also show a robot using our algorithm for performing a few reactive responses. †

show abstract

Section: Related Workmentioning

confidence: 99%

Anticipating Human Activities using Object Affordances for Reactive Robotic Response

Koppula

Saxena

2013

Robotics: Science and Systems IX

159

214

View full text Add to dashboard Cite

show abstract

“…Recent approaches use the learning of human intentions along with additional information, such as temporal relations between the actions [22,13] or object affordances [16] to improve the accuracy. Inverse Reinforcement Learning (IRL) has been used to predict 2D motions [42,18] or 3D human motions [5].…”

Section: A Intention-aware Motion Planning and Predictionmentioning

confidence: 99%

Intention-Aware Motion Planning Using Learning Based Human Motion Prediction

Park¹,

Park²,

Manocha

2017

Robotics: Science and Systems XIII

View full text Add to dashboard Cite

Abstract-We present a motion planning algorithm to compute collision-free and smooth trajectories for high-DOF robots interacting with humans in a shared workspace. Our approach uses offline learning of human actions along with temporal coherence to predict the human actions. Our intention-aware online planning algorithm uses the learned database to compute a reliable trajectory based on the predicted actions. We represent the predicted human motion using a Gaussian distribution and compute tight upper bounds on collision probabilities for safe motion planning. We highlight the performance of our planning algorithm in complex simulated scenarios and real world benchmarks with 7-DOF robot arms operating in a workspace with a human performing complex tasks. We demonstrate the benefits of our intention-aware planner in terms of computing safe trajectories in such uncertain environments.

show abstract

“…Ziebart et al [5] reported improved results with a predictor that models the intention and decision-making behavior of human motion. More recently, [6] included as features both physical properties of human motion and topological properties, and incorporated prediction of continuous trajectories.…”

Section: B Prediction Of Human Motionmentioning

confidence: 99%

Human-robot co-navigation using anticipatory indicators of human walking motion

Unhelkar

Pérez-D’Arpino

Stirling

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

Abstract-Mobile, interactive robots that operate in humancentric environments need the capability to safely and efficiently navigate around humans. This requires the ability to sense and predict human motion trajectories and to plan around them. In this paper, we present a study that supports the existence of statistically significant biomechanical turn indicators of human walking motions. Further, we demonstrate the effectiveness of these turn indicators as features in the prediction of human motion trajectories. Human motion capture data is collected with predefined goals to train and test a prediction algorithm. Use of anticipatory features results in improved performance of the prediction algorithm. Lastly, we demonstrate the closedloop performance of the prediction algorithm using an existing algorithm for motion planning within dynamic environments. The anticipatory indicators of human walking motion can be used with different prediction and/or planning algorithms for robotics; the chosen planning and prediction algorithm demonstrates one such implementation for human-robot conavigation.

show abstract

Feature-Based Prediction of Trajectories for Socially Compliant Navigation

Cited by 191 publications

References 25 publications

Anticipating Human Activities using Object Affordances for Reactive Robotic Response

Anticipating Human Activities using Object Affordances for Reactive Robotic Response

Intention-Aware Motion Planning Using Learning Based Human Motion Prediction

Human-robot co-navigation using anticipatory indicators of human walking motion

Contact Info

Product

Resources

About