Interactive Learning
Safe Interactive Movement Primitive Learning (SIMPLe),
Reference:
Interactive Imitation Learning of Bimanual Movement Primitives. arXiv:2210.16220 [cs.RO], 2022. .
Combining Interactive Teaching and Self-Exploration
Reference:
Solving Robot Assembly Tasks by Combining Interactive Teaching and Self-Exploration. arXiv:2209.11530 [cs.RO], 2022. .
Social Model Predictive Contouring Control (Social-MPCC)
Reference:
Visually-Guided Motion Planning for Autonomous Driving from Interactive Demonstrations. Engineering Applications of Artificial Intelligence, 116:105277, 2022. .
Interactive Learning of Stiffness and Attractors (ILoSA)
Reference:
ILoSA: Interactive Learning of Stiffness and Attractors. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7778–7785, 2021. .
Learning Interactively to Resolve Ambiguity (LIRA)
Reference:
Learning Interactively to Resolve Ambiguity in Reference Frame Selection. In 2020 Conference on Robot Learning (CoRL) (Jens Kober, Fabio Ramos, Claire Tomlin, eds.), PMLR, vol. 155 of Proceedings of Machine Learning Research, pp. 1298–1311, 2021. .
Teaching Imitative Policies in State-space (TIPS)
Reference:
Interactive Imitation Learning in State-Space. In 2020 Conference on Robot Learning (CoRL) (Jens Kober, Fabio Ramos, Claire Tomlin, eds.), PMLR, vol. 155 of Proceedings of Machine Learning Research, pp. 682–692, 2021. .
Interactive Learning of Temporal Features for Control
Reference:
Interactive Learning of Temporal Features for Control: Shaping Policies and State Representations From Human Feedback. IEEE Robotics & Automation Magazine, 27(2):46–54, 2020. .
Predictive Probabilistic Merging of Policies (PPMP)
Reference:
Deep Reinforcement Learning with Feedback-based Exploration. In IEEE Conference on Decision and Control (CDC), pp. 803–808, 2019. .
Gaussian Process Coach (GPC)
Reference:
Learning Gaussian Policies from Corrective Human Feedback. arXiv:1903.05216 [cs.LG], 2019. .
Enhanced Deep COACH (enhanced D-COACH)
Reference:
Continuous Control for High-Dimensional State Spaces: An Interactive Learning Approach. In IEEE International Conference on Robotics and Automation (ICRA), pp. 7611–7617, 2019. .
Deep COACH (D-COACH)
Reference:
Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks. In International Symposium on Experimental Robotics (ISER) (Jing Xiao, Torsten Kröger, Oussama Khatib, eds.), Springer International Publishing, pp. 353–363, 2018. .
Deep Reinforcement Learning
Fine-tuning Deep RL with Gradient-Free Optimization
Reference:
Fine-tuning Deep RL with Gradient-Free Optimization. In 21th IFAC World Congress, pp. 8049–8056, 2020. .
Experience Selection Baselines
Reference:
Experience Selection in Deep Reinforcement Learning for Control. Journal of Machine Learning Research, 19(9):1–56, 2018. .
Others
CONvergent Dynamics from demOnstRations (CONDOR)
Reference:
Stable Motion Primitives via Imitation and Contrastive Learning. arXiv:2302.10017 [cs.RO], 2023. .Framework for Benchmarking Gap Acceptance
Reference:
Disagreement-Aware Variable Impedance Control (DAVI)
Reference:
Disagreement-Aware Variable Impedance Control for Online Learning of Physical Human-Robot Cooperation Tasks. In ICRA 2022 full day workshop - Shared Autonomy in Physical Human-Robot Interaction: Adaptability and Trust, 2022. .
Learning Task-Parameterzied Skills from Few Demonstrations
Reference:
Learning Task-Parameterized Skills from Few Demonstrations. IEEE Robotics and Automation Letters, 7(2):4063–4070, 2022. The contents of this paper were also selected by ICRA'22 Program Committee for presentation at the Conference. .
Random Shadows and Highlights
Reference:
Random Shadows and Highlights: A New Data Augmentation Method for Extreme Lighting Conditions. arXiv:2101.05361 [cs.CV], 2021. .
Policy learning by Weighting Exploration with the Returns (PoWER)
A basic MATLAB/Octave implementation of Policy learning by Weighting Exploration with the Returns (PoWER) [4 variants: return or state-action value function, constant exploration or automatic adaptation], episodic Reward-Weighted Regression (eRWR), episodic Natural Actor Critic (eNAC), ‘Vanilla’ Policy Gradients (VPG), and Finite Difference Gradients (FDG): matlab_PoWER.zip
The required motor primitive code can be downloaded from http://www-clmc.usc.edu/Resources/Software backup April 26, 2020
Reference:
Policy Search for Motor Primitives in Robotics. Machine Learning, 84(1-2):171–203, 2011. .
DMPs for hitting and batting
A basic MATLAB/Octave implementation: hittingMP.m
Reference:
Movement Templates for Learning of Hitting and Batting. In IEEE International Conference on Robotics and Automation (ICRA), pp. 69–82, 2010. .