Analysis of Social Robotic Navigation approaches: CNN Encoder and Incremental Learning as an alternative to Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2008.07965v2
• Date: Sat, 5 Sep 2020 15:11:56 GMT
• Title: Analysis of Social Robotic Navigation approaches: CNN Encoder and Incremental Learning as an alternative to Deep Reinforcement Learning
• Authors: Janderson Ferreira (1), Agostinho A. F. J\'unior (1), Let\'icia Castro (1), Yves M. Galv\~ao (1), Pablo Barros (2), Bruno J. T. Fernandes (1) ((1) Universidade de Pernambuco - Escola Polit\'ecnica de Pernambuco, (2) Cognitive Architecture for Collaborative Technologies Unit - Istituto Italiano di Tecnologia)
• Abstract summary: Having humans in the learning loop is incompatible with state-of-the-art machine learning algorithms. In this work, we discuss this problem and possible solutions by analysing a previous study on adaptive convolutional encoders for a social navigation task.
• Score: 1.244705780038575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dealing with social tasks in robotic scenarios is difficult, as having humans in the learning loop is incompatible with most of the state-of-the-art machine learning algorithms. This is the case when exploring Incremental learning models, in particular the ones involving reinforcement learning. In this work, we discuss this problem and possible solutions by analysing a previous study on adaptive convolutional encoders for a social navigation task.

Related papers

• Online Context Learning for Socially-compliant Navigation [49.609656402450746]
  This letter introduces an online context learning method that aims to empower robots to adapt to new social environments online. Experiments using a community-wide simulator show that our method outperforms the state-of-the-art ones.
  arXiv  Detail & Related papers  (2024-06-17T12:59:13Z)
• RLIF: Interactive Imitation Learning as Reinforcement Learning [56.997263135104504]
  We show how off-policy reinforcement learning can enable improved performance under assumptions that are similar but potentially even more practical than those of interactive imitation learning. Our proposed method uses reinforcement learning with user intervention signals themselves as rewards. This relaxes the assumption that intervening experts in interactive imitation learning should be near-optimal and enables the algorithm to learn behaviors that improve over the potential suboptimal human expert.
  arXiv  Detail & Related papers  (2023-11-21T21:05:21Z)
• A Study on Learning Social Robot Navigation with Multimodal Perception [6.052803245103173]
  We present a study on learning social robot navigation with multimodal perception using a large-scale real-world dataset. We compare unimodal and multimodal learning approaches against a set of classical navigation approaches in different social scenarios. The results show that multimodal learning has a clear advantage over unimodal learning in both dataset and human studies.
  arXiv  Detail & Related papers  (2023-09-22T01:47:47Z)
• Incremental procedural and sensorimotor learning in cognitive humanoid robots [52.77024349608834]
  This work presents a cognitive agent that can learn procedures incrementally. We show the cognitive functions required in each substage and how adding new functions helps address tasks previously unsolved by the agent. Results show that this approach is capable of solving complex tasks incrementally.
  arXiv  Detail & Related papers  (2023-04-30T22:51:31Z)
• Fortuitous Forgetting in Connectionist Networks [20.206607130719696]
  We introduce "forget-and-relearn" as a powerful paradigm for shaping the learning trajectories of artificial neural networks. The forget-and-relearn framework unifies many existing iterative training algorithms in the image classification and language emergence literature. We leverage this understanding to improve upon existing algorithms by designing more targeted forgetting operations.
  arXiv  Detail & Related papers  (2022-02-01T00:15:58Z)
• Improving exploration in policy gradient search: Application to symbolic optimization [6.344988093245026]
  Many machine learning strategies leverage neural networks to search large spaces of mathematical symbols. In contrast to traditional evolutionary approaches, using a neural network at the core of the search allows learning higher-level symbolic patterns. We show that these techniques can improve the performance, increase sample efficiency, and lower the complexity of solutions for the task of symbolic regression.
  arXiv  Detail & Related papers  (2021-07-19T21:11:07Z)
• Ten Quick Tips for Deep Learning in Biology [116.78436313026478]
  Machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Deep learning has become its own subfield of machine learning. In the context of biological research, deep learning has been increasingly used to derive novel insights from high-dimensional biological data.
  arXiv  Detail & Related papers  (2021-05-29T21:02:44Z)
• XAI-N: Sensor-based Robot Navigation using Expert Policies and Decision Trees [55.9643422180256]
  We present a novel sensor-based learning navigation algorithm to compute a collision-free trajectory for a robot in dense and dynamic environments. Our approach uses deep reinforcement learning-based expert policy that is trained using a sim2real paradigm. We highlight the benefits of our algorithm in simulated environments and navigating a Clearpath Jackal robot among moving pedestrians.
  arXiv  Detail & Related papers  (2021-04-22T01:33:10Z)
• Reward Shaping with Subgoals for Social Navigation [7.6146285961466]
  Social navigation has been gaining attentions with the growth in machine intelligence. reinforcement learning can select an action in the prediction phase at a low computational cost. We propose a reward shaping method with subgoals to accelerate learning.
  arXiv  Detail & Related papers  (2021-04-13T13:52:58Z)
• Robot Navigation in a Crowd by Integrating Deep Reinforcement Learning and Online Planning [8.211771115758381]
  It is still an open and challenging problem for mobile robots navigating along time-efficient and collision-free paths in a crowd. Deep reinforcement learning is a promising solution to this problem. We propose a graph-based deep reinforcement learning method, SG-DQN. Our model can help the robot better understand the crowd and achieve a high success rate of more than 0.99 in the crowd navigation task.
  arXiv  Detail & Related papers  (2021-02-26T02:17:13Z)
• Hierarchical Affordance Discovery using Intrinsic Motivation [69.9674326582747]
  We propose an algorithm using intrinsic motivation to guide the learning of affordances for a mobile robot. This algorithm is capable to autonomously discover, learn and adapt interrelated affordances without pre-programmed actions. Once learned, these affordances may be used by the algorithm to plan sequences of actions in order to perform tasks of various difficulties.
  arXiv  Detail & Related papers  (2020-09-23T07:18:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.