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Unsupervised Visuomotor Control through Distributional Planning Networks
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s10514-015-9459-7openalex-snapshot
Learning state representations with robotic priors
inferredcites →https://doi.org/10.1109/cvprw.2017.69openalex-snapshot
Time-Contrastive Networks: Self-Supervised Learning from Multi-view Observation
inferredcites →https://doi.org/10.1109/icra.2016.7487173openalex-snapshot
Deep spatial autoencoders for visuomotor learning
inferredcites →https://doi.org/10.1109/icra.2016.7487517openalex-snapshot
Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours
inferredcites →https://doi.org/10.1109/icra.2017.7989324openalex-snapshot
Deep visual foresight for planning robot motion
inferredcites →https://doi.org/10.1109/icra.2018.8463189openalex-snapshot
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
inferredcites →https://doi.org/10.1109/ijcnn.2012.6252823openalex-snapshot
Autonomous reinforcement learning on raw visual input data in a real world application
inferredcites →https://doi.org/10.1109/iros.1999.813033openalex-snapshot
Image-based simultaneous control of robot and target object motions by direct-image-interpretation method
inferredcites →https://doi.org/10.1109/iros.2012.6386109openalex-snapshot
MuJoCo: A physics engine for model-based control
inferredcites →https://doi.org/10.1109/iros.2017.8206046openalex-snapshot
Deep predictive policy training using reinforcement learning
inferredcites →https://doi.org/10.1109/lra.2018.2800101openalex-snapshot
Integrating State Representation Learning Into Deep Reinforcement Learning
inferredcites →https://doi.org/10.1145/1015330.1015430openalex-snapshot
Apprenticeship learning via inverse reinforcement learning
inferredcites →https://doi.org/10.1145/1015330.1015441openalex-snapshot
Learning low dimensional predictive representations
inferredcites →https://doi.org/10.1177/0278364911404092openalex-snapshot
Closing the learning-planning loop with predictive state representations
inferredcites →https://doi.org/10.1177/0278364917710318openalex-snapshot
Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
inferredcites →https://doi.org/10.1184/r1/6555512openalex-snapshot
Maximum Entropy Inverse Reinforcement Learning
inferredcites →https://doi.org/10.15607/rss.2015.xi.012openalex-snapshot
DeepMPC: Learning Deep Latent Features for Model Predictive Control
inferredcites →https://doi.org/10.15607/rss.2017.xiii.050openalex-snapshot
Unsupervised Perceptual Rewards for Imitation Learning
inferredcites →https://doi.org/10.23943/princeton/9780691210780.003.0015openalex-snapshot
The Elephant in the Room
inferredcites →https://doi.org/10.4230/oasics.dx.2026.15openalex-snapshot
AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)
inferredcites →https://doi.org/10.48550/arxiv.1312.6114openalex-snapshot
Auto-Encoding Variational Bayes
inferredcites →https://doi.org/10.48550/arxiv.1603.00448openalex-snapshot
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
inferredcites →https://doi.org/10.48550/arxiv.1603.06277openalex-snapshot
Composing graphical models with neural networks for structured representations and fast inference
inferredcites →https://doi.org/10.48550/arxiv.1612.07307openalex-snapshot
Loss is its own Reward: Self-Supervision for Reinforcement Learning
inferredcites →https://doi.org/10.48550/arxiv.1703.03078openalex-snapshot
Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning
inferredcites →https://doi.org/10.48550/arxiv.1703.05407openalex-snapshot
Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play
inferredcites →https://doi.org/10.48550/arxiv.1703.07718openalex-snapshot
Independently Controllable Features
inferredcites →https://doi.org/10.48550/arxiv.1704.06888openalex-snapshot
Time-Contrastive Networks: Self-Supervised Learning from Video
inferredcites →https://doi.org/10.48550/arxiv.1705.09045openalex-snapshot
Cross-Domain Perceptual Reward Functions
inferredcites →https://doi.org/10.48550/arxiv.1705.09805openalex-snapshot
PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations
inferredcites →https://doi.org/10.48550/arxiv.1708.01289openalex-snapshot
Independently Controllable Factors
inferredcites →https://doi.org/10.48550/arxiv.1709.05185openalex-snapshot
Unsupervised state representation learning with robotic priors: a robustness benchmark
inferredcites →https://doi.org/10.48550/arxiv.1710.00489openalex-snapshot
SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and Control
inferredcites →https://doi.org/10.48550/arxiv.1710.11252openalex-snapshot
Stochastic Variational Video Prediction
inferredcites →https://doi.org/10.48550/arxiv.1802.09081openalex-snapshot
Temporal Difference Models: Model-Free Deep RL for Model-Based Control
inferredcites →https://doi.org/10.48550/arxiv.1806.10293openalex-snapshot
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
inferredcites →https://doi.org/10.48550/arxiv.1807.04742openalex-snapshot
Visual Reinforcement Learning with Imagined Goals
inferredcites →https://doi.org/10.48550/arxiv.1810.00482openalex-snapshot
Few-Shot Goal Inference for Visuomotor Learning and Planning
inferredcites →https://doi.org/10.48550/arxiv.1812.05905openalex-snapshot
Soft Actor-Critic Algorithms and Applications
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1038/s41467-020-15533-0openalex-snapshot
Representation of visual uncertainty through neural gain variability
inferred← citeshttps://doi.org/10.1109/cyber46603.2019.9066512openalex-snapshot
Deep Adversarial Imitation Learning of Locomotion Skills from One-shot Video Demonstration
inferred← citeshttps://doi.org/10.1109/estream.2019.8732166openalex-snapshot
Implementation of Machine Learning Algorithms for Autonomous Robot Trajectory Resolving
inferred← citeshttps://doi.org/10.1109/icdl49984.2021.9515624openalex-snapshot
REAL 2021 – Robot open-Ended Autonomous Learning: A Competition and Benchmark
inferred← citeshttps://doi.org/10.1109/icra40945.2020.9196582openalex-snapshot
Adversarial Skill Networks: Unsupervised Robot Skill Learning from Video
inferred← citeshttps://doi.org/10.1109/icra40945.2020.9197020openalex-snapshot
Scalable Multi-Task Imitation Learning with Autonomous Improvement
inferred← citeshttps://doi.org/10.1109/icra46639.2022.9812369openalex-snapshot
Learning to Optimize in Model Predictive Control
inferred← citeshttps://doi.org/10.1109/icra48506.2021.9560752openalex-snapshot
Goal-Conditioned End-to-End Visuomotor Control for Versatile Skill Primitives
inferred← citeshttps://doi.org/10.1109/icra48506.2021.9561749openalex-snapshot
Introspective Visuomotor Control: Exploiting Uncertainty in Deep Visuomotor Control for Failure Recovery
inferred← citeshttps://doi.org/10.1109/iros51168.2021.9636362openalex-snapshot
DMotion: Robotic Visuomotor Control with Unsupervised Forward Model Learned from Videos
inferred← citeshttps://doi.org/10.1109/lra.2019.2956365openalex-snapshot
Self-Supervised Correspondence in Visuomotor Policy Learning
inferred← citeshttps://doi.org/10.1109/lra.2020.2968032openalex-snapshot
Learning Transformable and Plannable se(3) Features for Scene Imitation of a Mobile Service Robot
inferred← citeshttps://doi.org/10.1109/lra.2021.3071062openalex-snapshot
Planning-Augmented Hierarchical Reinforcement Learning
inferred← citeshttps://doi.org/10.1109/tcds.2023.3270081openalex-snapshot
REAL-X—Robot Open-Ended Autonomous Learning Architecture: Building Truly End-to-End Sensorimotor Autonomous Learning Systems
inferred← citeshttps://doi.org/10.15607/rss.2020.xvi.101openalex-snapshot
Learning Task-Driven Control Policies via Information Bottlenecks
inferred← citeshttps://doi.org/10.48550/arxiv.1905.07861openalex-snapshot
Perceptual Values from Observation
inferred← citeshttps://doi.org/10.48550/arxiv.1905.07866openalex-snapshot
Reinforcement Learning without Ground-Truth State
inferred← citeshttps://doi.org/10.48550/arxiv.1909.12830openalex-snapshot
The Differentiable Cross-Entropy Method
inferred← citeshttps://doi.org/10.48550/arxiv.1910.11215openalex-snapshot
RoboNet: Large-Scale Multi-Robot Learning
inferred← citeshttps://doi.org/10.48550/arxiv.1912.02807openalex-snapshot
Combining Q-Learning and Search with Amortized Value Estimates
inferred← citeshttps://doi.org/10.48550/arxiv.1912.07670openalex-snapshot
To Follow or not to Follow: Selective Imitation Learning from Observations
inferred← citeshttps://doi.org/10.48550/arxiv.2004.02860openalex-snapshot
Weakly-Supervised Reinforcement Learning for Controllable Behavior
inferred← citeshttps://doi.org/10.48550/arxiv.2004.10190openalex-snapshot
Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning
inferred← citeshttps://doi.org/10.48550/arxiv.2005.03648openalex-snapshot
Plan2Vec: Unsupervised Representation Learning by Latent Plans
inferred← citeshttps://doi.org/10.48550/arxiv.2110.10255openalex-snapshot
A Simple Approach to Continual Learning by Transferring Skill Parameters
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