CM
C. Muench
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
3 records found
1
We show how to design a motion prediction algorithm that works with 3D object detections and map locations. In particular, we obtain object id’s – even though the training data does not contain any object id’s – across multiple time-steps into the future by propagating a Gaussian Mixture of likely object (e.g., vehicle) locations through time.We validate our approach on the nuScenes dataset. First, we find that a motion prediction algorithm without tracking id’s performs as well as motion prediction algorithm with tracking id’s in the training data. Second, the 3D labels of an on-board perception system are inferior (e.g., loss of detections, positional uncertainty) to those generated by offline labelling (automatic labelling pipeline, manual labelling). Even so, we find that a moderate increase in the size of the training data offsets the deterioration in prediction performance (with no additional offline labelling).
...
We show how to design a motion prediction algorithm that works with 3D object detections and map locations. In particular, we obtain object id’s – even though the training data does not contain any object id’s – across multiple time-steps into the future by propagating a Gaussian Mixture of likely object (e.g., vehicle) locations through time.We validate our approach on the nuScenes dataset. First, we find that a motion prediction algorithm without tracking id’s performs as well as motion prediction algorithm with tracking id’s in the training data. Second, the 3D labels of an on-board perception system are inferior (e.g., loss of detections, positional uncertainty) to those generated by offline labelling (automatic labelling pipeline, manual labelling). Even so, we find that a moderate increase in the size of the training data offsets the deterioration in prediction performance (with no additional offline labelling).
Modeling possible future outcomes of robot-human interactions is of importance in the intelligent vehicle and mobile robotics domains. Knowing the reward function that explains the observed behavior of a human agent is advantageous for modeling the behavior with Markov Decision Processes (MDPs). However, learning the rewards that determine the observed actions from data is complicated by interactions. We present a novel inverse reinforcement learning (IRL) algorithm that can infer the reward function in multi-Agent interactive scenarios. In particular, the agents may act boundedly rational (i.e., sub-optimal), a characteristic that is typical for human decision making. Additionally, every agent optimizes its own reward function which makes it possible to address non-cooperative setups. In contrast to other methods, the algorithm does not rely on reinforcement learning during inference of the parameters of the reward function. We demonstrate that our proposed method accurately infers the ground truth reward function in two-Agent interactive experiments.1
...
Modeling possible future outcomes of robot-human interactions is of importance in the intelligent vehicle and mobile robotics domains. Knowing the reward function that explains the observed behavior of a human agent is advantageous for modeling the behavior with Markov Decision Processes (MDPs). However, learning the rewards that determine the observed actions from data is complicated by interactions. We present a novel inverse reinforcement learning (IRL) algorithm that can infer the reward function in multi-Agent interactive scenarios. In particular, the agents may act boundedly rational (i.e., sub-optimal), a characteristic that is typical for human decision making. Additionally, every agent optimizes its own reward function which makes it possible to address non-cooperative setups. In contrast to other methods, the algorithm does not rely on reinforcement learning during inference of the parameters of the reward function. We demonstrate that our proposed method accurately infers the ground truth reward function in two-Agent interactive experiments.1
We propose a novel algorithm that predicts the interaction of pedestrians with cars within a Markov Decision Process framework. It leverages the fact that Q-functions may be composed in the maximum-entropy framework, thus the solutions of two sub-tasks may be combined to approximate the full interaction problem. Sub-task one is the interaction-free navigation of a pedestrian in an urban environment and sub-task two is the interaction with an approaching car (deceleration, waiting etc.) without accounting for the environmental context (e.g. street layout). We propose a regularization scheme motivated by the soft-Bellman-equations and illustrate its necessity. We then analyze the properties of the algorithm in detail with a toy model. We find that as long as the interaction-free sub-task is modelled well with a Q-function, we can learn a representation of the interaction between a pedestrian and a car.
...
We propose a novel algorithm that predicts the interaction of pedestrians with cars within a Markov Decision Process framework. It leverages the fact that Q-functions may be composed in the maximum-entropy framework, thus the solutions of two sub-tasks may be combined to approximate the full interaction problem. Sub-task one is the interaction-free navigation of a pedestrian in an urban environment and sub-task two is the interaction with an approaching car (deceleration, waiting etc.) without accounting for the environmental context (e.g. street layout). We propose a regularization scheme motivated by the soft-Bellman-equations and illustrate its necessity. We then analyze the properties of the algorithm in detail with a toy model. We find that as long as the interaction-free sub-task is modelled well with a Q-function, we can learn a representation of the interaction between a pedestrian and a car.