DoorGym: A Scalable Door Opening Environment and Baseline Agent
Urakami et al.: https://arxiv.org/pdf/1908.01887v1.pdf
#DeepLearning #ReinforcementLearning #Robotics
Urakami et al.: https://arxiv.org/pdf/1908.01887v1.pdf
#DeepLearning #ReinforcementLearning #Robotics
Learning Vision-based Flight in Drone Swarms by Imitation
Schilling et al.: https://arxiv.org/abs/1908.02999
#Robotics #MachineLearning #MultiagentSystems
Schilling et al.: https://arxiv.org/abs/1908.02999
#Robotics #MachineLearning #MultiagentSystems
arXiv.org
Learning Vision-based Flight in Drone Swarms by Imitation
Decentralized drone swarms deployed today either rely on sharing of positions
among agents or detecting swarm members with the help of visual markers. This
work proposes an entirely visual...
among agents or detecting swarm members with the help of visual markers. This
work proposes an entirely visual...
Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real
Nachum et al.: https://arxiv.org/abs/1908.05224
#Robotics #ArtificialIntelligence #MachineLearning
Nachum et al.: https://arxiv.org/abs/1908.05224
#Robotics #ArtificialIntelligence #MachineLearning
Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation
Suraj Nair and Chelsea Finn
Paper: https://arxiv.org/abs/1909.05829
Code: https://github.com/google-research/google-research/tree/master/hierarchical_foresight
#MachineLearning #ArtificialIntelligence #Robotics #ReinforcementLearning
Suraj Nair and Chelsea Finn
Paper: https://arxiv.org/abs/1909.05829
Code: https://github.com/google-research/google-research/tree/master/hierarchical_foresight
#MachineLearning #ArtificialIntelligence #Robotics #ReinforcementLearning
Deep Dynamics Models for Learning Dexterous Manipulation
Nagabandi et al.: https://arxiv.org/abs/1909.11652
#Robotics #MachineLearning #ReinforcementLearning
Nagabandi et al.: https://arxiv.org/abs/1909.11652
#Robotics #MachineLearning #ReinforcementLearning
arXiv.org
Deep Dynamics Models for Learning Dexterous Manipulation
Dexterous multi-fingered hands can provide robots with the ability to flexibly perform a wide range of manipulation skills. However, many of the more complex behaviors are also notoriously...
End-to-End Motion Planning of Quadrotors Using Deep Reinforcement Learning
Efe Camci and Erdal Kayacan : https://arxiv.org/abs/1909.13599
#Robotics #ArtificialIntelligence #ReinforcementLearning
Efe Camci and Erdal Kayacan : https://arxiv.org/abs/1909.13599
#Robotics #ArtificialIntelligence #ReinforcementLearning
Bayesian Optimization Meets Riemannian Manifolds in Robot Learning
Jaquier et al.: https://arxiv.org/abs/1910.04998
#BayesianOptimization #Robotics #MachineLearning
Jaquier et al.: https://arxiv.org/abs/1910.04998
#BayesianOptimization #Robotics #MachineLearning
Quantized Reinforcement Learning (QUARL)
Srivatsan Krishnan, Sharad Chitlangia, Maximilian Lam, Zishen Wan, Aleksandra Faust, Vijay Janapa Reddi : https://arxiv.org/abs/1910.01055
Code: https://github.com/harvard-edge/quarl
#DeepLearning #ReinforcementLearning #Quantization #Robotics
Srivatsan Krishnan, Sharad Chitlangia, Maximilian Lam, Zishen Wan, Aleksandra Faust, Vijay Janapa Reddi : https://arxiv.org/abs/1910.01055
Code: https://github.com/harvard-edge/quarl
#DeepLearning #ReinforcementLearning #Quantization #Robotics
arXiv.org
QuaRL: Quantization for Fast and Environmentally Sustainable...
Deep reinforcement learning continues to show tremendous potential in achieving task-level autonomy, however, its computational and energy demands remain prohibitively high. In this paper, we...
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
Yu et al.: https://arxiv.org/abs/1910.10897
#MachineLearning #ArtificialIntelligence #Robotics
Yu et al.: https://arxiv.org/abs/1910.10897
#MachineLearning #ArtificialIntelligence #Robotics
arXiv.org
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta...
Meta-reinforcement learning algorithms can enable robots to acquire new skills much more quickly, by leveraging prior experience to learn how to learn. However, much of the current research on...
Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning
Gupta et al.: https://arxiv.org/abs/1910.11956
Website : https://relay-policy-learning.github.io
#ReinforcementLearning #MachineLearning #Robotics
Gupta et al.: https://arxiv.org/abs/1910.11956
Website : https://relay-policy-learning.github.io
#ReinforcementLearning #MachineLearning #Robotics
arXiv.org
Relay Policy Learning: Solving Long-Horizon Tasks via Imitation...
We present relay policy learning, a method for imitation and reinforcement learning that can solve multi-stage, long-horizon robotic tasks. This general and universally-applicable, two-phase...