Predicting actions to act predictably: Cooperative partial motion planning with maximum entropy models

Pfeiffer, Mark G.; Schwesinger, Ulrich; Sommer, Hannes; Galceran, Enric; Siegwart, Roland

doi:10.1109/iros.2016.7759329

Cited by 94 publications

(91 citation statements)

References 20 publications

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“…However, their approach has only been tested in scripted environments with no more than four humans and due to the featurebased joint modeling, it scales poorly with the number of agents considered. Very recently, [24] extended this approach to unseen and unstructured environments using a receding horizon motion planning approach.…”

Section: A Modeling Human Interactions For Navigationmentioning

confidence: 99%

Social Attention: Modeling Attention in Human Crowds

Vemula

Muelling

2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

598

469

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Robots that navigate through human crowds need to be able to plan safe, efficient, and human predictable trajectories. This is a particularly challenging problem as it requires the robot to predict future human trajectories within a crowd where everyone implicitly cooperates with each other to avoid collisions. Previous approaches to human trajectory prediction have modeled the interactions between humans as a function of proximity. However, that is not necessarily true as some people in our immediate vicinity moving in the same direction might not be as important as other people that are further away, but that might collide with us in the future. In this work, we propose Social Attention, a novel trajectory prediction model that captures the relative importance of each person when navigating in the crowd, irrespective of their proximity. We demonstrate the performance of our method against a state-of-the-art approach on two publicly available crowd datasets and analyze the trained attention model to gain a better understanding of which surrounding agents humans attend to, when navigating in a crowd.

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Section: A Modeling Human Interactions For Navigationmentioning

confidence: 99%

Social Attention: Modeling Attention in Human Crowds

Vemula

Muelling

2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

598

469

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show abstract

“…Yet, all previous approaches require the knowledge of the precise localization and velocity information of nearby pedestrians. This limitation restricts these methods to be only applicable for robots equipped with high precision sensors, like 3D Lidars [2], [3]. Moreover, the estimation of the state information of pedestrians is generally timeconsuming.…”

mentioning

confidence: 99%

Socially Compliant Navigation Through Raw Depth Inputs with Generative Adversarial Imitation Learning

Tai

Zhang

Liu

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

178

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We present an approach for mobile robots to learn to navigate in dynamic environments with pedestrians via raw depth inputs, in a socially compliant manner. To achieve this, we adopt a generative adversarial imitation learning (GAIL) strategy, which improves upon a pre-trained behavior cloning policy. Our approach overcomes the disadvantages of previous methods, as they heavily depend on the full knowledge of the location and velocity information of nearby pedestrians, which not only requires specific sensors, but also the extraction of such state information from raw sensory input could consume much computation time. In this paper, our proposed GAIL-based model performs directly on raw depth inputs and plans in real-time. Experiments show that our GAIL-based approach greatly improves the safety and efficiency of the behavior of mobile robots from pure behavior cloning. The real-world deployment also shows that our method is capable of guiding autonomous vehicles to navigate in a socially compliant manner directly through raw depth inputs. In addition, we release a simulation plugin for modeling pedestrian behaviors based on the social force model.

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“…Abbeel et al [9] present an IRLbased approach where they teach an autonomous car to navigate in parking lots by observing human demonstrations. Similarly, Pfeiffer et al [10] and Kretzschmar et al [11] present approaches for navigation in dynamic environments based on IRL. By observing pedestrian motion, a probability distribution over pedestrian trajectories is found.…”

Section: A Learning By Demonstrationmentioning

confidence: 99%

Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Mapless Navigation by Leveraging Prior Demonstrations

Pfeiffer

Shukla

Turchetta

et al. 2018

IEEE Robot. Autom. Lett.

Self Cite

157

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This work presents a case study of a learning-based approach for target driven map-less navigation. The underlying navigation model is an end-to-end neural network which is trained using a combination of expert demonstrations, imitation learning (IL) and reinforcement learning (RL). While RL and IL suffer from a large sample complexity and the distribution mismatch problem, respectively, we show that leveraging prior expert demonstrations for pre-training can reduce the training time to reach at least the same level of performance compared to plain RL by a factor of 5. We present a thorough evaluation of different combinations of expert demonstrations, different RL algorithms and reward functions, both in simulation and on a real robotic platform. Our results show that the final model outperforms both standalone approaches in the amount of successful navigation tasks. In addition, the RL reward function can be significantly simplified when using pre-training, e.g. by using a sparse reward only. The learned navigation policy is able to generalize to unseen and real-world environments.

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Predicting actions to act predictably: Cooperative partial motion planning with maximum entropy models

Cited by 94 publications

References 20 publications

Social Attention: Modeling Attention in Human Crowds

Social Attention: Modeling Attention in Human Crowds

Socially Compliant Navigation Through Raw Depth Inputs with Generative Adversarial Imitation Learning

Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Mapless Navigation by Leveraging Prior Demonstrations

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