An adaptive deep reinforcement learning framework enables curling robots with human-like performance in real-world conditions

Won, Dong Ok; Müller, Klaus Robert; Lee, Seong Whan

doi:10.1126/scirobotics.abb9764

Cited by 65 publications

(36 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More and more research efforts start to combine data-driven learning algorithms with rigorous scientific or engineering theory to yield novel insights and applications. 9 , 15 , 351 …”

Section: Machine Learning Tutorial and Intersections With Chemistrymentioning

confidence: 99%

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

et al. 2021

Self Cite

View full text Add to dashboard Cite

Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.

show abstract

“…More and more research efforts start to combine data-driven learning algorithms with rigorous scientific or engineering theory to yield novel insights and applications. 9 , 15 , 351 …”

Section: Machine Learning Tutorial and Intersections With Chemistrymentioning

confidence: 99%

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…In sequential decision making, the action of the previous step will affect the next step. Many problems can be represented in this form, such as game playing [15]- [17], autonomous driving [18]- [20], robot control [21], [22], recommended system [23] and trading [24]. Inspired by this work, we also adopt the classic architecture in which the policy network guides the MCTS.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Policy for Non-prehensile Multi-object Rearrangement with Deep Reinforcement Learning and Monte Carlo Tree Search

Bai¹,

Meng²,

Liu³

et al. 2021

Preprint

View full text Add to dashboard Cite

Non-prehensile multi-object rearrangement is a robotic task of planning feasible paths and transferring multiple objects to their predefined target poses without grasping. It needs to consider how each object reaches the target and the order of object movement, which significantly deepens the complexity of the problem. To address these challenges, we propose a hierarchical policy to divide and conquer for non-prehensile multi-object rearrangement. In the high-level policy, guided by a designed policy network, the Monte Carlo Tree Search efficiently searches for the optimal rearrangement sequence among multiple objects, which benefits from imitation and reinforcement. In the low-level policy, the robot plans the paths according to the order of path primitives and manipulates the objects to approach the goal poses one by one. We verify through experiments that the proposed method can achieve a higher success rate, fewer steps, and shorter path length compared with the state-of-the-art.

show abstract

“…D EEP Neural Networks (DNNs) have achieved significant success over the years, helping to advance Artificial Intelligence (AI). Driven by the exponential growth in available data and computational resources, DNNs achieve stateof-the-art results across various fields of Machine Learning (ML), such as Computer Vision (CV) [1], [2], [3], Natural Language Processing (NLP) [4], [5], [6], [7], and Reinforcement Learning (RL) [8], [9], [10] i : Aignostics, Berlin, Germany j : RIKEN AIP, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, Japan…”

Section: Introductionmentioning

confidence: 99%

“…Artificial Intelligence [11], there are already specific domains where DNNs could surpass human performance, such as gameplaying or image recognition tasks [12], [13], [14], [10]. DNNs accomplish such high performance by learning mappings from raw data to meaningful representations.…”

Section: Introductionmentioning

confidence: 99%

Explaining Bayesian Neural Networks

Bykov,

Höhne,

Creosteanu

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

To make advanced learning machines such as Deep Neural Networks (DNNs) more transparent in decision making, explainable AI (XAI) aims to provide interpretations of DNNs' predictions. These interpretations are usually given in the form of heatmaps, each one illustrating relevant patterns regarding the prediction for a given instance. Bayesian approaches such as Bayesian Neural Networks (BNNs) so far have a limited form of transparency (model transparency) already built-in through their prior weight distribution, but notably, they lack explanations of their predictions for given instances. In this work, we bring together these two perspectives of transparency into a holistic explanation framework for explaining BNNs. Within the Bayesian framework, the network weights follow a probability distribution. Hence, the standard (deterministic) prediction strategy of DNNs extends in BNNs to a predictive distribution, and thus the standard explanation extends to an explanation distribution. Exploiting this view, we uncover that BNNs implicitly employ multiple heterogeneous prediction strategies. While some of these are inherited from standard DNNs, others are revealed to us by considering the inherent uncertainty in BNNs. Our quantitative and qualitative experiments on toy/benchmark data and realworld data from pathology show that the proposed approach of explaining BNNs can lead to more effective and insightful explanations.

show abstract

An adaptive deep reinforcement learning framework enables curling robots with human-like performance in real-world conditions

Cited by 65 publications

References 70 publications

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Hierarchical Policy for Non-prehensile Multi-object Rearrangement with Deep Reinforcement Learning and Monte Carlo Tree Search

Explaining Bayesian Neural Networks

Contact Info

Product

Resources

About