Research Hub

    Papers with a full citation graph - what each paper draws on, and who cites it.

    All (51)robotics (6)deep learning (5)backpropagation (3)deep-learning (3)HRI (3)survey (3)algorithms (3)reasoning (2)actuators (2)machine-learning (2)
    Show 177 more topics
    optimization (2)LSTM (2)deep reinforcement learning (2)biped robot (2)representation-learning (2)force control (2)ensemble (2)regression (2)hypothesis-testing (2)statistics (2)autonomous agents (1)web environment (1)evaluation (1)UI automation (1)ReAct (1)function calling (1)agent (1)chain-of-thought (1)prompting (1)agents (1)sensors (1)review (1)automatic-differentiation (1)calculus (1)pose estimation (1)openpose (1)part affinity fields (1)computer vision (1)skeletal tracking (1)OCR (1)text recognition (1)CRNN (1)CTC (1)locomotion (1)Sim-to-Real (1)Transformer (1)attention (1)NLP (1)machine translation (1)object detection (1)YOLO (1)real-time vision (1)bounding boxes (1)gradient-descent (1)adam-optimizer (1)DQN (1)Q-learning (1)Atari games (1)transfer learning (1)feature extraction (1)CNNs (1)generalization (1)vector-spaces (1)word-embeddings (1)cosine-similarity (1)nlp (1)CNN (1)ImageNet (1)image classification (1)IMU (1)orientation filter (1)quaternion (1)ROS (1)Robot Operating System (1)open-source (1)middleware (1)service robot (1)elderly care (1)robot arm (1)imitation learning (1)learning from demonstration (1)recommender systems (1)matrix factorization (1)SVD (1)collaborative filtering (1)Netflix Prize (1)safety (1)collaborative robots (1)standards (1)robotic hand (1)pneumatic actuators (1)force sensor (1)design (1)ZMP (1)balance (1)control (1)human-robot interaction (1)social robots (1)service robotics (1)Random Forest (1)decision tree (1)classification (1)biped walking (1)inverted pendulum (1)gait pattern (1)humanoid robot (1)random forest (1)decision trees (1)supervised learning (1)motion planning (1)RRT (1)random tree (1)impedance (1)hybrid control (1)search engines (1)PageRank (1)eigenvectors (1)graph theory (1)Markov chains (1)localization (1)particle filter (1)navigation (1)mobile robot (1)mechanical design (1)ASIMO (1)humanoid (1)RNN (1)sequence modeling (1)vanishing gradient (1)visual servoing (1)image-based (1)position-based (1)lasso (1)regularization (1)feature selection (1)cross-validation (1)model evaluation (1)bootstrap (1)model selection (1)reinforcement learning (1)temporal difference (1)prediction (1)TD (1)edge detection (1)image processing (1)Canny filter (1)hysteresis (1)neural networks (1)representations (1)neural-networks (1)credit-assignment (1)convex-optimization (1)linear-programming (1)interior-point-methods (1)computational-complexity (1)least squares (1)history of statistics (1)Gauss (1)matrix multiplication (1)computational complexity (1)numerical linear algebra (1)K-Means (1)clustering (1)unsupervised learning (1)data analysis (1)logistic regression (1)binary data (1)maximum likelihood (1)kinematics (1)Denavit-Hartenberg (1)transformation matrix (1)markov-chain (1)monte-carlo (1)mcmc (1)statistical-mechanics (1)PID (1)Ziegler-Nichols (1)tuning (1)industrial control (1)statistical-significance (1)neyman-pearson (1)decision-theory (1)t-distribution (1)sample-size (1)bayes-theorem (1)probability (1)history-of-mathematics (1)

    2 papers

    2017arXiv preprint arXiv:1707.02286 (presented at NeurIPS 2017)

    Emergence of Locomotion Behaviours in Rich Environments

    Jay Lemay, David Silver, Nicolas Heess, Srinivasan Sriram, Darren King, Koray Kavukcuoglu

    Finding: The trained policies exhibited emergent behaviors like walking, running, jumping, and even leaping across various environments, demonstrating robust locomotion.

    2015Nature, vol. 518, no. 7540, pp. 529-533, DOI: 10.1038/nature14236

    Human-level control through deep reinforcement learning

    Diederik Kingma, Ioannis Antonoglou, Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Martin Riedmiller, Alex Graves

    Finding: DQN achieved human-level or better performance on 49 Atari games, marking the first major success of Deep RL.