Deep Learning Quadcopter Control via Risk-Aware Active Learning
Authors: Olov Andersson, Mariusz Wzorek, Patrick Doherty
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the efficacy of the approach on a difficult collision avoidance problem with non-cooperative moving obstacles. Our findings indicate that the resulting neural network approximations are least 50 times faster than the trajectory optimizer while still satisfying the safety requirements. We demonstrate the potential of the approach by implementing a synthesized deep neural network policy on the nano-quadcopter microcontroller. |
| Researcher Affiliation | Academia | Olov Andersson, Mariusz Wzorek, Patrick Doherty {olov.a.andersson, mariusz.wzorek, patrick.doherty}@liu.se Department of Computer and Information Science Link oping University, SE-58183 Link oping, Sweden |
| Pseudocode | No | The paper includes 'Figure 1: Training procedure for deep neural network policy approximations,' which is a flowchart, but no structured pseudocode or algorithm blocks are present. |
| Open Source Code | No | The paper mentions using Tensorflow (www.tensorflow.org) and the open-source Crazyflie platform (www.bitcraze.io), but does not explicitly state that the authors' own implementation code for the methodology is open-source or provide a link to it. |
| Open Datasets | No | The paper states, 'Training batches contained about 500 000 examples from the trajectory optimizer, which for our problems took approximately 12 hours to generate,' indicating the data was generated by the authors using a trajectory optimizer rather than being from a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper mentions 'early stopping' during training, which implies the use of a validation set, but it does not provide specific dataset split information (e.g., percentages or sample counts for training, validation, and test sets). |
| Hardware Specification | Yes | The networks were implemented in Tensorflow1, a graph-based language for numerical computation, and trained using a consumer Geforce GTX970 GPU. |
| Software Dependencies | No | The paper mentions software like 'Tensorflow' and 'Free RTOS' but does not provide specific version numbers for any key software components or libraries. |
| Experiment Setup | Yes | We use three hidden layers for each problem size, with the network architectures 10-200-200-200-2 and 18-400-400-400-2 respectively. ... Training batches contained about 500 000 examples ... We used the ADAM (Kingma and Ba 2015) stochastic gradient algorithm with mini-batches of size 500 and early stopping. Even just a small amount of dropout (Srivastava et al. 2014), e.g. 1-5%, seemed to be helpful for the larger problems. |